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[] | Poster | [] | Hypernetworks, neural networks that predict the parameters of another neural network, are powerful models that have been successfully used in diverse applications from image generation to multi-task learning. Unfortunately, existing hypernetworks are often challenging to train. Training typically converges far more slowly than for non-hypernetwork models, and the rate of convergence can be very sensitive to hyperparameter choices. In this work, we identify a fundamental and previously unidentified problem that contributes to the challenge of training hypernetworks: a magnitude proportionality between the inputs and outputs of the hypernetwork. We demonstrate both analytically and empirically that this can lead to unstable optimization, thereby slowing down convergence, and sometimes even preventing any learning. We present a simple solution to this problem using a revised hypernetwork formulation that we call Magnitude Invariant Parametrizations (MIP). We demonstrate the proposed solution on several hypernetwork tasks, where it consistently stabilizes training and achieves faster convergence. Furthermore, we perform a comprehensive ablation study including choices of activation function, normalization strategies, input dimensionality, and hypernetwork architecture; and find that MIP improves training in all scenarios. We provide easy-to-use code that can turn existing networks into MIP-based hypernetworks. | [] | [] | Magnitude Invariant Parametrizations Improve Hypernetwork Learning | [
"Jose Javier Gonzalez Ortiz",
"John Guttag",
"Adrian V Dalca"
] | 2304.07645 | 18,191 | https://openreview.net/forum?id=fJNnerz6iH |
|
[] | Spotlight Poster | [] | This paper presents the first theoretical guarantee for Bayesian bilevel optimization (BBO) that we term for the prevalent bilevel framework combining Bayesian optimization at the outer level to tune hyperparameters, including the inner-level stochastic gradient descent (SGD). We prove sublinear regret bounds suggesting simultaneous convergence of the inner-level model parameters and outer-level hyperparameters to optimal configurations for generalization capability. A pivotal, technical novelty in the proofs is modeling the excess risk of the SGD-trained parameters as evaluation noise during Bayesian optimization. Our theory implies the inner unit horizon, defined as the number of SGD iterations, shapes the convergence behavior of BBO. This suggests practical guidance on configuring the inner unit horizon to enhance training efficiency and model performance. | [] | [] | Convergence of Bayesian Bilevel Optimization | [
"Shi Fu",
"Fengxiang He",
"Xinmei Tian",
"Dacheng Tao"
] | 18,190 | https://openreview.net/forum?id=fLXpXa7iiz |
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[] | Spotlight Poster | [] | Program synthesis aims to create accurate, executable code from natural language descriptions. This field has leveraged the power of reinforcement learning (RL) in conjunction with large language models (LLMs), significantly enhancing code generation capabilities. This integration focuses on directly optimizing functional correctness, transcending conventional supervised losses. While current literature predominantly favors policy-based algorithms, attributes of program synthesis suggest a natural compatibility with value-based methods. This stems from rich collection of off-policy programs developed by human programmers, and the straightforward verification of generated programs through automated unit testing (i.e. easily obtainable rewards in RL language). Diverging from the predominant use of policy-based algorithms, our work explores the applicability of value-based approaches, leading to the development of our $\mathcal{B}$-Coder (pronounced Bellman coder). Yet, training value-based methods presents challenges due to the enormous search space inherent to program synthesis. To this end, we propose an initialization protocol for RL agents utilizing pre-trained LMs and a conservative Bellman operator to reduce training complexities. Moreover, we demonstrate how to leverage the learned value functions as a dual strategy to post-process generated programs. Our empirical evaluations demonstrated $\mathcal{B}$-Coder's capability in achieving state-of-the-art performance compared with policy-based methods. Remarkably, this achievement is reached with minimal reward engineering effort, highlighting the effectiveness of value-based RL, independent of reward designs. | [] | [] | $\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis | [
"Zishun Yu",
"Yunzhe Tao",
"Liyu Chen",
"Tao Sun",
"Hongxia Yang"
] | 18,189 | https://openreview.net/forum?id=fLf589bx1f |
||
[] | Poster | [
"https://github.com/zjunlp/PitfallsKnowledgeEditing"
] | As the cost associated with fine-tuning Large Language Models (LLMs) continues to rise, recent research efforts have pivoted towards developing methodologies to edit implicit knowledge embedded within LLMs. Yet, there's still a dark cloud lingering overhead -- will knowledge editing trigger butterfly effect? since it is still unclear whether knowledge editing might introduce side effects that pose potential risks or not. This paper pioneers the investigation into the potential pitfalls associated with knowledge editing for LLMs. To achieve this, we introduce new benchmark datasets and propose innovative evaluation metrics. Our results underline two pivotal concerns: (1) Knowledge Conflict: Editing groups of facts that logically clash can magnify the inherent inconsistencies in LLMs—a facet neglected by previous methods. (2) Knowledge Distortion: Altering parameters with the aim of editing factual knowledge can irrevocably warp the innate knowledge structure of LLMs. Experimental results vividly demonstrate that knowledge editing might inadvertently cast a shadow of unintended consequences on LLMs, which warrant attention and efforts for future works. Codes are in the supplementary materials and will be released. | [] | [] | Unveiling the Pitfalls of Knowledge Editing for Large Language Models | [
"Zhoubo Li",
"Ningyu Zhang",
"Yunzhi Yao",
"Mengru Wang",
"Xi Chen",
"Huajun Chen"
] | 2310.02129 | 18,188 | https://openreview.net/forum?id=fNktD3ib16 |
|
[] | Poster | [] | This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks. In the context of dense matching, many works benefit from one of two forms of aggregation: feature aggregation, which pertains to the alignment of similar features, or cost aggregation, a procedure aimed at instilling coherence in the flow estimates across neighboring pixels. In this work, we first show that feature aggregation and cost aggregation exhibit distinct characteristics and reveal the potential for substantial benefits stemming from the judicious use of both aggregation processes. We then introduce a simple yet effective architecture that harnesses self- and cross-attention mechanisms to show that our approach unifies feature aggregation and cost aggregation and effectively harnesses the strengths of both techniques. Within the proposed attention layers, the features and cost volume both complement each other, and the attention layers are interleaved through a coarse-to-fine design to further promote accurate correspondence estimation. Finally at inference, our network produces multi-scale predictions, computes their confidence scores, and selects the most confident flow for final prediction. Our framework is evaluated on standard benchmarks for semantic matching, and also applied to geometric matching, where we show that our approach achieves significant improvements compared to existing methods. | [] | [] | Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence | [
"Sunghwan Hong",
"Seokju Cho",
"Seungryong Kim",
"Stephen Lin"
] | 2403.11120 | 18,187 | https://openreview.net/forum?id=fQHb1uZzl7 |
|
[
"facebook/nougat-small",
"facebook/nougat-base"
] | Poster | [
"https://github.com/facebookresearch/nougat"
] | Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human- readable documents and machine-readable text. We release the models and code to accelerate future work on scientific text recognition. | [
"ysharma/nougat",
"merve/nougat-transformers"
] | [] | Nougat: Neural Optical Understanding for Academic Documents | [
"Lukas Blecher",
"Guillem Cucurull",
"Thomas Scialom",
"Robert Stojnic"
] | 2308.13418 | 18,185 | https://openreview.net/forum?id=fUtxNAKpdV |
|
[] | Poster | [] | Transformer-based machine translation techniques currently dominate the field of program translation. However, these models pose challenges in explaining program translations. Moreover, researchers frequently invest substantial time and computational resources in retraining models, yet the improvement in translation accuracy is quite limited.To address these issues, we introduce a novel approach, $k\text{NN-ECD}$, which combines $k$-nearest-neighbor search with a key-value error correction datastore to overwrite the wrong translations of TransCoder-ST. This provides a decision-making basis for interpreting the corrected translations. Building upon this, we further propose $k\text{NN-ECS}_{m}$, a methodology that employs a distributed structure with $m$ sub-datastores connected in series, utilizing $m$ diverse experts for multi-round error correction. Additionally, we put forward a unified name rule, encouraging the datastore to focus more on code logic and structure rather than diverse rare identifiers. Our experimental results show that our approach improves the translation accuracy from 68.9\% to 89.9\% of TransCoder-ST (for translation from Java to Python). This error correction method augments program translation, overcoming the inherent limitations of Transformer-based code translation models, such as resource-intensive retraining requirements and uninterpretable outcomes. | [] | [] | An interpretable error correction method for enhancing code-to-code translation | [
"Min Xue",
"Artur Andrzejak",
"Marla Leuther"
] | 18,184 | https://openreview.net/forum?id=fVxIEHGnVT |
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[] | Spotlight Poster | [] | Weakly supervised learning aims to construct effective predictive models from imperfectly labeled data. The recent trend of weakly supervised learning has focused on how to learn an accurate classifier from completely unlabeled data, given little supervised information such as class priors. In this paper, we consider a newly proposed weakly supervised learning problem called multi-class classification from multiple unlabeled datasets, where only multiple sets of unlabeled data and their class priors (i.e., the proportions of each class) are provided for training the classifier. To solve this problem, we first propose a classifier-consistent method (CCM) based on a probability transition matrix. However, CCM cannot guarantee risk consistency and lacks of purified supervision information during training. Therefore, we further propose a risk-consistent method (RCM) that progressively purifies supervision information during training by importance weighting. We provide comprehensive theoretical analyses for our methods to demonstrate the statistical consistency. Experimental results on multiple benchmark datasets and various prior matrices demonstrate the superiority of our proposed methods. | [] | [] | Consistent Multi-Class Classification from Multiple Unlabeled Datasets | [
"Zixi Wei",
"Senlin Shu",
"Yuzhou Cao",
"Hongxin Wei",
"Bo An",
"Lei Feng"
] | 18,183 | https://openreview.net/forum?id=fW7DOHDQvF |
||
[] | Poster | [] | Generative Large Language Models (LLMs) have achieved remarkable advancements in various NLP tasks. However, these advances have not been reflected in the translation task, especially those with moderate model sizes (i.e., 7B or 13B parameters), which still lag behind conventional supervised encoder-decoder translation models. Previous studies have attempted to improve the translation capabilities of these LLMs, but their gains have been limited. In this study, we propose a novel fine-tuning approach for LLMs that is specifically designed for the translation task, eliminating the need for the abundant parallel data that traditional translation models usually depend on.Our approach consists of two fine-tuning stages: initial fine-tuning on monolingual data followed by subsequent fine-tuning on a small set of high-quality parallel data. We introduce the LLM developed through this strategy as **A**dvanced **L**anguage **M**odel-based tr**A**nslator (**ALMA**). Based on LLaMA-2 as our underlying model, our results show that the model can achieve an average improvement of more than 12 BLEU and 12 COMET over its zero-shot performance across 10 translation directions from the WMT'21 (2 directions) and WMT'22 (8 directions) test datasets. The performance is significantly better than all prior work and even superior to the NLLB-54B model \citep{nllb} and GPT-3.5-text-davinci-003, with only 7B or 13B parameters. This method establishes the foundation for a novel training paradigm in machine translation. | [] | [] | A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models | [
"Haoran Xu",
"Young Jin Kim",
"Amr Sharaf",
"Hany Hassan Awadalla"
] | 2309.11674 | 18,182 | https://openreview.net/forum?id=farT6XXntP |
|
[] | Poster | [] | Due to privacy or patent concerns, a growing number of large models are released without granting access to their training data, making transferring their knowledge inefficient and problematic. In response, Data-Free Knowledge Distillation (DFKD) methods have emerged as direct solutions. However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain). The degradation stems from the portions of teachers' knowledge that are not applicable to the student domain. They are specific to the teacher domain and would undermine students' performance. Hence, selectively transferring teachers' appropriate knowledge becomes the primary challenge in DFKD. In this work, we propose a simple but effective method AuG-KD. It utilizes an uncertainty-guided and sample-specific anchor to align student-domain data with the teacher domain and leverages a generative method to progressively trade off the learning process between OOD knowledge distillation and domain-specific information learning via mixup learning. Extensive experiments in 3 datasets and 8 settings demonstrate the stability and superiority of our approach. | [] | [] | AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation | [
"Zihao TANG",
"Zheqi Lv",
"Shengyu Zhang",
"Yifan Zhou",
"Xinyu Duan",
"Fei Wu",
"Kun Kuang"
] | 18,181 | https://openreview.net/forum?id=fcqWJ8JgMR |
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[] | Poster | [
"https://github.com/jinpz/refactor"
] | Human mathematicians are often good at recognizing modular and reusable theorems that make complex mathematical results within reach. In this paper, we propose a novel method called theoREm-from-prooF extrACTOR (REFACTOR) for training neural networks to mimic this ability in formal mathematical theorem proving. We show on a set of unseen proofs, REFACTOR is able to extract 19.6\% of the theorems that humans would use to write the proofs. When applying the model to the existing Metamath library, REFACTOR extracted 16 new theorems. With newly extracted theorems, we show that the existing proofs in the MetaMath database can be refactored. The new theorems are used very frequently after refactoring, with an average usage of 733.5 times, and help shorten the proof lengths. Lastly, we demonstrate that the prover trained on the new-theorem refactored dataset proves more test theorems and outperforms state-of-the-art baselines by frequently leveraging a diverse set of newly extracted theorems. | [] | [] | REFACTOR: Learning to Extract Theorems from Proofs | [
"Jin Peng Zhou",
"Yuhuai Wu",
"Qiyang Li",
"Roger Baker Grosse"
] | 2402.17032 | 18,178 | https://openreview.net/forum?id=fgKjiVrm6u |
|
[] | Poster | [] | We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. Our questions allow simple, fast, and factual verification. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92\% vs. 15\% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA's philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system's capability to exhibit similar robustness as the average human does on such questions. Using GAIA's methodology, we devise 466 questions and their answer. We release our questions while retaining answers to 300 of them to power a leader-board \href{https://huggingface.co/xxx}{hereby accessible}. | [] | [] | GAIA: a benchmark for General AI Assistants | [
"Grégoire Mialon",
"Clémentine Fourrier",
"Thomas Wolf",
"Yann LeCun",
"Thomas Scialom"
] | 2311.12983 | 18,176 | https://openreview.net/forum?id=fibxvahvs3 |
|
[] | Poster | [] | We study the optimisation problem associated with Gaussian process regression using squared loss. The most common approach to this problem is to apply an exact solver, such as conjugate gradient descent, either directly on the problem or on a reduced-order version of it. However, stochastic gradient descent has recently gained traction in the Gaussian process literature, driven largely by its successes in deep learning. In this paper, we show that this approach when done right---by which we mean using specific insights from the optimisation and kernel communities---is highly effective.We thus introduce a particular stochastic dual gradient descent algorithm, conveniently implementable with a few lines of code using any deep learning framework. We explain our design decisions by illustrating their advantage against alternatives with ablation studies.We then show that the new method is highly competitive: our evaluations on standard regression benchmarks and a Bayesian optimisation task set our approach apart from conjugate gradients, variational Gaussian process approximations, and a prior version of stochastic gradient descent tailored for Gaussian processes. On a molecular binding affinity prediction task, our method places Gaussian process regression on par in terms of performance with graph neural networks. | [] | [] | Stochastic Gradient Descent for Gaussian Processes Done Right | [
"Jihao Andreas Lin",
"Shreyas Padhy",
"Javier Antoran",
"Austin Tripp",
"Alexander Terenin",
"Csaba Szepesvari",
"José Miguel Hernández-Lobato",
"David Janz"
] | 2310.20581 | 18,175 | https://openreview.net/forum?id=fj2E5OcLFn |
|
[] | Poster | [] | Hard-thresholding is an important type of algorithm in machine learning that is used to solve $\ell_0$ constrained optimization problems. However, the true gradient of the objective function can be difficult to access in certain scenarios, which normally can be approximated by zeroth-order (ZO) methods. SZOHT algorithm is the only algorithm tackling $\ell_0$ sparsity constraints with zeroth-order gradients so far. Unfortunately, SZOHT has a notable limitation on the number of random directions due to the inherent conflict between the deviation of ZO gradients and the expansivity of the hard-thresholding operator. This paper approaches this problem by considering the role of variance and provides a new insight into variance reduction: mitigating the unique conflicts between ZO gradients and hard-thresholding. Under this perspective, we propose a generalized variance reduced ZO hard-thresholding algorithm as well as the generalized convergence analysis under standard assumptions. The theoretical results demonstrate the new algorithm eliminates the restrictions on the number of random directions, leading to improved convergence rates and broader applicability compared with SZOHT. Finally, we illustrate the utility of our method on a portfolio optimization problem as well as black-box adversarial attacks. | [] | [] | New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions | [
"Xinzhe Yuan",
"William de Vazelhes",
"Bin Gu",
"Huan Xiong"
] | 18,174 | https://openreview.net/forum?id=fjf3YenThE |
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[] | Spotlight Poster | [
"https://github.com/liuyuan-pal/SyncDreamer"
] | In this paper, we present a novel diffusion model called SyncDreamer that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views from a single-view image of an object. However, maintaining consistency in geometry and colors for the generated images remains a challenge. To address this issue, we propose a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a single reverse process. SyncDreamer synchronizes the intermediate states of all the generated images at every step of the reverse process through a 3D-aware feature attention mechanism that correlates the corresponding features across different views. Experiments show that SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D. | [] | [] | SyncDreamer: Generating Multiview-consistent Images from a Single-view Image | [
"Yuan Liu",
"Cheng Lin",
"Zijiao Zeng",
"Xiaoxiao Long",
"Lingjie Liu",
"Taku Komura",
"Wenping Wang"
] | 2309.03453 | 18,828 | https://openreview.net/forum?id=MN3yH2ovHb |
|
[] | Poster | [] | This work considers a rather general and broad class of Markov chains, Ito chains that look like Euler-Maryama discretization of some Stochastic Differential Equation. The chain we study is a unified framework for theoretical analysis. It comes with almost arbitrary isotropic and state-dependent noise instead of normal and state-independent one, as in most related papers. Moreover, the drift and diffusion coefficient in our chain can be inexact to cover a wide range of applications such as Stochastic Gradient Langevin Dynamics, sampling, Stochastic Gradient Descent, or Stochastic Gradient Boosting. We prove the bound in $\mathcal{W}_2$-distance between the laws of our Ito chain and the corresponding differential equation. These results improve or cover most of the known estimates. Moreover, for some particular cases, our analysis is the first. | [] | [] | Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting | [
"Aleksei Ustimenko",
"Aleksandr Beznosikov"
] | 2310.06081 | 18,173 | https://openreview.net/forum?id=fjpfCOV4ru |
|
[] | Poster | [] | The accurate modeling of dynamics in interactive environments is critical for successful long-range prediction. Such a capability could advance Reinforcement Learning (RL) and Planning algorithms, but achieving it is challenging. Inaccuracies in model estimates can compound, resulting in increased errors over long horizons.We approach this problem from the lens of Koopman theory, where the nonlinear dynamics of the environment can be linearized in a high-dimensional latent space. This allows us to efficiently parallelize the sequential problem of long-range prediction using convolution while accounting for the agent's action at every time step.Our approach also enables stability analysis and better control over gradients through time. Taken together, these advantages result in significant improvement over the existing approaches, both in the efficiency and the accuracy of modeling dynamics over extended horizons. We also show that this model can be easily incorporated into dynamics modeling for model-based planning and model-free RL and report promising experimental results. | [] | [] | Efficient Dynamics Modeling in Interactive Environments with Koopman Theory | [
"Arnab Kumar Mondal",
"Siba Smarak Panigrahi",
"Sai Rajeswar",
"Kaleem Siddiqi",
"Siamak Ravanbakhsh"
] | 2306.11941 | 18,172 | https://openreview.net/forum?id=fkrYDQaHOJ |
|
[] | Poster | [
"https://github.com/ruili33/SEC"
] | Despite the promising few-shot ability of large language models (LLMs), the standard paradigm of In-context Learning (ICL) suffers the disadvantages of susceptibility to selected demonstrations and the intricacy to generate these demonstrations. In this paper, we raise the fundamental question that whether human-generated demonstrations are necessary for ICL. To answer this question, we propose self-contemplation prompting strategy (SEC), a paradigm free from human-crafted demonstrations. The key point of SEC is that, instead of using hand-crafted examples as demonstrations in ICL, SEC asks LLMs to first create demonstrations on their own, based on which the final output is generated. SEC is a flexible framework and can be adapted to both the vanilla ICL and the chain-of-thought (CoT), but with greater ease: as the manual-generation process of both examples and rationale can be saved. Extensive experiments in arithmetic reasoning, commonsense reasoning, multi-task language understanding, and code generation benchmarks, show that SEC, which does not require hand-crafted demonstrations, significantly outperforms the zero-shot learning strategy, and achieves comparable results to ICL with hand-crafted demonstrations. This demonstrates that, for many tasks, contemporary LLMs possess a sufficient level of competence to exclusively depend on their own capacity for decision making, removing the need for external training data. | [] | [] | Are Human-generated Demonstrations Necessary for In-context Learning? | [
"Rui Li",
"Guoyin Wang",
"Jiwei Li"
] | 2309.14681 | 18,169 | https://openreview.net/forum?id=frRDT6EOhg |
|
[] | Poster | [] | Unsupervised domain adaptation (UDA) involves adapting a model trained on a label-rich source domain to an unlabeled target domain. However, in real-world scenarios, the absence of target-domain labels makes it challenging to evaluate the performance of UDA models. Furthermore, prevailing UDA methods relying on adversarial training and self-training could lead to model degeneration and negative transfer, further exacerbating the evaluation problem. In this paper, we propose a novel metric called the Transfer Score to address these issues. The proposed metric enables the unsupervised evaluation of UDA models by assessing the spatial uniformity of the classifier via model parameters, as well as the transferability and discriminability of deep representations. Based on the metric, we achieve three novel objectives without target-domain labels: (1) selecting the best UDA method from a range of available options, (2) optimizing hyperparameters of UDA models to prevent model degeneration, and (3) identifying which checkpoint of UDA model performs optimally. Our work bridges the gap between data-level UDA research and practical UDA scenarios, enabling a realistic assessment of UDA model performance. We validate the effectiveness of our metric through extensive empirical studies on UDA datasets of different scales and imbalanced distributions. The results demonstrate that our metric robustly achieves the aforementioned goals. | [] | [] | Can We Evaluate Domain Adaptation Models Without Target-Domain Labels? | [
"Jianfei Yang",
"Hanjie Qian",
"Yuecong Xu",
"Kai Wang",
"Lihua Xie"
] | 2305.18712 | 18,167 | https://openreview.net/forum?id=fszrlQ2DuP |
|
[] | Poster | [] | Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. In peripheral areas such as the prediction of molecular properties, fully connected attention networks have been shown to be successful. However, unlike these finite atom arrangements, crystal structures are infinitely repeating, periodic arrangements of atoms, whose fully connected attention results in infinitely connected attention. In this work, we show that this infinitely connected attention can lead to a computationally tractable and physically interpretable formulation. We then propose a simple yet effective transformer-based encoder architecture for crystal structures called Crystalformer. Compared with an existing transformer-based model, the proposed model requires only 38% of number of parameters per attention block. Despite the architectural simplicity, the proposed method outperforms state-of-the-art methods for various property regression tasks on the Materials Project and JARVIS-DFT datasets. | [] | [] | Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding | [
"Tatsunori Taniai",
"Ryo Igarashi",
"Yuta Suzuki",
"Naoya Chiba",
"Kotaro Saito",
"Yoshitaka Ushiku",
"Kanta Ono"
] | 2403.11686 | 18,164 | https://openreview.net/forum?id=fxQiecl9HB |
|
[] | Poster | [] | Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients without sharing their data. While being a flexible framework, where the distribution of local data, participation rate, and computing power of each client can greatly vary, such flexibility gives rise to many new challenges, especially in the hyperparameter tuning on the client side. We propose $\Delta$-SGD, a simple step size rule for SGD that enables each client to use its own step size by adapting to the local smoothness of the function each client is optimizing. We provide theoretical and empirical results where the benefit of the client adaptivity is shown in various FL scenarios. | [] | [] | Adaptive Federated Learning with Auto-Tuned Clients | [
"Junhyung Lyle Kim",
"Taha Toghani",
"Cesar A Uribe",
"Anastasios Kyrillidis"
] | 2306.11201 | 18,162 | https://openreview.net/forum?id=g0mlwqs8pi |
|
[] | Poster | [] | The Euler Characteristic Transform (ECT) has proven to be a powerful representation, combining geometrical and topological characteristics of shapes and graphs. However, the ECT was hitherto unable to learn task-specific representations. We overcome this issue and develop a novel computational layer that enables learning the ECT in an end-to-end fashion. Our method, DECT, is fast and computationally efficient, while exhibiting performance on a par with more complex models in both graph and point cloud classification tasks. Moreover, we show that this seemingly unexpressive statistic still provides the same topological expressivity as more complex topological deep learning layers provide. | [] | [] | Differentiable Euler Characteristic Transforms for Shape Classification | [
"Ernst Röell",
"Bastian Rieck"
] | 2310.07630 | 18,824 | https://openreview.net/forum?id=MO632iPq3I |
|
[] | Poster | [] | Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent connection between diffusion models and MTL, there remains an unexplored area in designing neural architectures that explicitly incorporate MTL into the framework of diffusion models. In this paper, we present Denoising Task Routing (DTR), a simple add-on strategy for existing diffusion model architectures to establish distinct information pathways for individual tasks within a single architecture by selectively activating subsets of channels in the model. What makes DTR particularly compelling is its seamless integration of prior knowledge of denoising tasks into the framework: (1) Task Affinity: DTR activates similar channels for tasks at adjacent timesteps and shifts activated channels as sliding windows through timesteps, capitalizing on the inherent strong affinity between tasks at adjacent timesteps. (2) Task Weights: During the early stages (higher timesteps) of the denoising process, DTR assigns a greater number of task-specific channels, leveraging the insight that diffusion models prioritize reconstructing global structure and perceptually rich contents in earlier stages, and focus on simple noise removal in later stages. Our experiments reveal that DTR not only consistently boosts diffusion models' performance across different evaluation protocols without adding extra parameters but also accelerates training convergence. Finally, we show the complementarity between our architectural approach and existing MTL optimization techniques, providing a more complete view of MTL in the context of diffusion training. Significantly, by leveraging this complementarity, we attain matched performance of DiT-XL using the smaller DiT-L with a reduction in training iterations from 7M to 2M. Our project page is available at https://byeongjun-park.github.io/DTR/ | [] | [] | Denoising Task Routing for Diffusion Models | [
"Byeongjun Park",
"Sangmin Woo",
"Hyojun Go",
"Jin-Young Kim",
"Changick Kim"
] | 2310.07138 | 18,818 | https://openreview.net/forum?id=MY0qlcFcUg |
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[] | Poster | [] | We study both stream-based and pool-based active learning with neural network approximations. A recent line of works proposed bandit-based approaches that transformed active learning into a bandit problem, achieving both theoretical and empirical success. However, the performance and computational costs of these methods may be susceptible to the number of classes, denoted as $K$, due to this transformation. Therefore, this paper seeks to answer the question: "How can we mitigate the adverse impacts of $K$ while retaining the advantages of principled exploration and provable performance guarantees in active learning?" To tackle this challenge, we propose two algorithms based on the newly designed exploitation and exploration neural networks for stream-based and pool-based active learning. Subsequently, we provide theoretical performance guarantees for both algorithms in a non-parametric setting, demonstrating a slower error-growth rate concerning $K$ for the proposed approaches. We use extensive experiments to evaluate the proposed algorithms, which consistently outperform state-of-the-art baselines. | [] | [] | Neural Active Learning Beyond Bandits | [
"Yikun Ban",
"Ishika Agarwal",
"Ziwei Wu",
"Yada Zhu",
"Kommy Weldemariam",
"Hanghang Tong",
"Jingrui He"
] | 2404.12522 | 18,161 | https://openreview.net/forum?id=g1S72T3FGc |
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[] | Poster | [] | Spiking Neural Networks (SNNs) have garnered considerable attention due to their energy efficiency and unique biological characteristics. However, the widely adopted Leaky Integrate-and-Fire (LIF) model, as the mainstream neuron model in current SNN research, has been revealed to exhibit significant deficiencies in deep-layer gradient calculation and capturing global information on the time dimension. In this paper, we propose the Learnable Multi-hierarchical (LM-H) model to address these issues by dynamically regulating its membrane-related factors. We point out that the LM-H model fully encompasses the information representation range of the LIF model while offering the flexibility to adjust the extraction ratio between historical and current information. Additionally, we theoretically demonstrate the effectiveness of the LM-H model and the functionality of its internal parameters, and propose a progressive training algorithm tailored specifically for the LM-H model. Furthermore, we devise an efficient training framework for our novel advanced model, encompassing hybrid training and time-slicing online training. Through extensive experiments on various datasets, we validate the remarkable superiority of our model and training algorithm compared to previous state-of-the-art approaches. Code is available at [https://github.com/hzc1208/STBP_LMH](https://github.com/hzc1208/STBP_LMH). | [] | [] | A Progressive Training Framework for Spiking Neural Networks with Learnable Multi-hierarchical Model | [
"Zecheng Hao",
"Xinyu Shi",
"Zihan Huang",
"Tong Bu",
"Zhaofei Yu",
"Tiejun Huang"
] | 18,160 | https://openreview.net/forum?id=g52tgL8jy6 |
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[] | Poster | [] | Extending the class descriptions (i.e., prompts) with related attributes, e.g., using brown sparrow instead of sparrow, improves the performance of vision-language models like CLIP for image classification. However, current methods select a subset of attributes regardless of commonalities between the target classes, potentially providing no useful information that would have helped to distinguish between them. For instance, they may use color instead of bill shape to distinguish between sparrows and wrens, which are both brown. We propose Follow-up Differential Descriptions (FuDD), a zero-shot approach that tailors the class descriptions to each dataset and leads to additional attributes that better differentiate the target classes. FuDD first identifies the ambiguous classes for each image, and then uses a Large Language Model (LLM) to generate new class descriptions that differentiate between them. The new class descriptions resolve the initial ambiguity and help predict the correct label. In our experiments, FuDD consistently outperforms generic description ensembles and naive LLM-generated descriptions on 12 datasets. We show that differential descriptions are an effective tool to resolve class ambiguities, which otherwise significantly degrade the performance. We also show that high quality natural language class descriptions produced by FuDD result in comparable performance to few-shot adaptation methods. | [] | [] | Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification | [
"Reza Esfandiarpoor",
"Stephen Bach"
] | 2311.07593 | 18,158 | https://openreview.net/forum?id=g6rZtxaXRm |
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[] | Poster | [] | Second-order optimization has been developed to accelerate the training of deep neural networks and it is being applied to increasingly larger-scale models. In this study, towards training on further larger scales, we identify a specific parameterization for second-order optimization that promotes feature learning in a stable manner even if the network width increases significantly. Inspired by a maximal update parametrization, we consider a one-step update of the gradient and reveal the appropriate scales of hyperparameters including random initialization, learning rates, and damping terms. Our approach covers two major second-order optimization algorithms, K-FAC and Shampoo, and we demonstrate that our parametrization achieves higher generalization performance in feature learning.In particular, it enables us to transfer the hyperparameters across models with different widths. | [] | [] | On the Parameterization of Second-Order Optimization Effective towards the Infinite Width | [
"Satoki Ishikawa",
"Ryo Karakida"
] | 2312.12226 | 18,156 | https://openreview.net/forum?id=g8sGBSQjYk |
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[] | Spotlight Poster | [] | Latest insights from biology show that intelligence not only emerges from the connections between neurons, but that individual neurons shoulder more computational responsibility than previously anticipated. Specifically, neural plasticity should be critical in the context of constantly changing reinforcement learning (RL) environments, yet current approaches still primarily employ static activation functions. In this work, we motivate the use of adaptable activation functions in RL and show that rational activation functions are particularly suitable for augmenting plasticity. Inspired by residual networks, we derive a condition under which rational units are closed under residual connections and formulate a naturally regularised version. The proposed joint-rational activation allows for desirable degrees of flexibility, yet regularises plasticity to an extent that avoids overfitting by leveraging a mutual set of activation function parameters across layers. We demonstrate that equipping popular algorithms with (joint) rational activations leads to consistent improvements on different games from the Atari Learning Environment benchmark, notably making DQN competitive to DDQN and Rainbow. | [] | [] | Adaptive Rational Activations to Boost Deep Reinforcement Learning | [
"Quentin Delfosse",
"Patrick Schramowski",
"Martin Mundt",
"Alejandro Molina",
"Kristian Kersting"
] | 2102.09407 | 18,155 | https://openreview.net/forum?id=g90ysX1sVs |
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[] | Spotlight Poster | [] | Instruction fine-tuning has recently emerged as a promising approach for improving the zero-shot capabilities of Large Language Models (LLMs) on new tasks. This technique has shown particular strength in improving the performance of modestly sized LLMs, sometimes inducing performance competitive with much larger model variants. In this paper, we ask two questions: (1) How sensitive are instruction-tuned models to the particular phrasings of instructions, and, (2) How can we make them more robust to such natural language variation? To answer the former, we collect a set of 319 instructions manually written by NLP practitioners for over 80 unique tasks included in widely used benchmarks, and we evaluate the variance and average performance of these instructions as compared to instruction phrasings observed during instruction fine-tuning. We find that using novel (unobserved) but appropriate instruction phrasings consistently degrades model performance, sometimes substantially so. Further, such natural instructions yield a wide variance in downstream performance, despite their semantic equivalence. Put another way, instruction-tuned models are not especially robust to instruction re-phrasings. We propose a simple method to mitigate this issue by introducing ``soft prompt'' embedding parameters and optimizing these to maximize the similarity between representations of semantically equivalent instructions. We show that this method consistently improves the robustness of instruction-tuned models. | [] | [] | Evaluating the Zero-shot Robustness of Instruction-tuned Language Models | [
"Jiuding Sun",
"Chantal Shaib",
"Byron C Wallace"
] | 2306.11270 | 18,154 | https://openreview.net/forum?id=g9diuvxN6D |
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[] | Poster | [
"https://github.com/OpenGVLab/LLMPrune-BESA"
] | Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc. While their performance is impressive, the computational footprint due to their vast number of parameters can be prohibitive. Existing solutions such as SparseGPT and Wanda attempt to alleviate this issue through weight pruning. However, their layer-wise approach results in significant perturbation to the model's output and requires meticulous hyperparameter tuning, such as the pruning rate, which can adversely affect overall model performance. To address this, this paper introduces a novel LLM pruning technique dubbed blockwise parameter-efficient sparsity allocation (BESA) by applying a blockwise reconstruction loss. In contrast to the typical layer-wise pruning techniques, BESA is characterized by two distinctive attributes: i) it targets the overall pruning error with respect to individual transformer blocks, and ii) it allocates layer-specific sparsity in a differentiable manner, both of which ensure reduced performance degradation after pruning. Our experiments show that BESA achieves state-of-the-art performance, efficiently pruning LLMs like LLaMA1, and LLaMA2 with 7B to 70B parameters on a single A100 GPU in just five hours. Code is available at [here](https://github.com/LinkAnonymous/BESA). | [] | [] | BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation | [
"Peng Xu",
"Wenqi Shao",
"Mengzhao Chen",
"Shitao Tang",
"Kaipeng Zhang",
"Peng Gao",
"Fengwei An",
"Yu Qiao",
"Ping Luo"
] | 2402.16880 | 18,153 | https://openreview.net/forum?id=gC6JTEU3jl |
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[] | Poster | [] | Recently, text watermarking algorithms for large language models (LLMs) have been proposed to mitigate the potential harms of text generated by LLMs, including fake news and copyright issues. However, current watermark detection algorithms require the secret key used in the watermark generation process, making them susceptible to security breaches and counterfeiting.To address this limitation, we propose the first private watermarking algorithm that uses two different neural networks for watermark generation and detection, instead of using the same key at both stages. Meanwhile, the token embedding parameters are shared between the generation and detection networks, which makes the detection network achieve a high accuracy very efficiently.Experiments demonstrate that Our algorithm attains high detection accuracy and computational efficiency through neural networks with a minimized number of parameters. Subsequent analysis confirms the high complexity involved in reverse-engineering the watermark generation algorithms from the detection network. | [] | [] | An Unforgeable Publicly Verifiable Watermark for Large Language Models | [
"Aiwei Liu",
"Leyi Pan",
"Xuming Hu",
"Shuang Li",
"Lijie Wen",
"Irwin King",
"Philip S. Yu"
] | 2307.16230 | 18,145 | https://openreview.net/forum?id=gMLQwKDY3N |
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[] | Poster | [] | Recent advances in self-supervised learning and the Transformer architecture have significantly improved natural language processing (NLP), achieving remarkably low perplexity.However, the growing size of NLP models introduces a memory wall problem during the generation phase.To mitigate this issue, recent efforts have focused on quantizing model weights to sub-4-bit precision while preserving full precision for activations, resulting in practical speed-ups during inference on a single GPU.However, these improvements primarily stem from reduced memory movement, which necessitates a resource-intensive dequantization process rather than actual computational reduction.In this paper, we introduce LUT-GEMM, an efficient kernel for quantized matrix multiplication, which not only eliminates the resource-intensive dequantization process but also reduces computational costs compared to previous kernels for weight-only quantization.Furthermore, we proposed group-wise quantization to offer a flexible trade-off between compression ratio and accuracy.The impact of LUT-GEMM is facilitated by implementing high compression ratios through low-bit quantization and efficient LUT-based operations.We show experimentally that when applied to the OPT-175B model with 3-bit quantization, LUT-GEMM substantially accelerates token generation latency, achieving a remarkable 2.1x improvement on a single GPU when compared to OPTQ, which relies on the costly dequantization process. | [] | [] | LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models | [
"Gunho Park",
"Baeseong park",
"Minsub Kim",
"Sungjae Lee",
"Jeonghoon Kim",
"Beomseok Kwon",
"Se Jung Kwon",
"Byeongwook Kim",
"Youngjoo Lee",
"Dongsoo Lee"
] | 18,146 | https://openreview.net/forum?id=gLARhFLE0F |
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[] | Poster | [] | Adversarial training is a widely used strategy for making neural networks resistant to adversarial perturbations. For a neural network of width $m$, $n$ input training data in $d$ dimension, it takes $\Omega(mnd)$ time cost per training iteration for the forward and backward computation. In this paper we analyze the convergence guarantee of adversarial training procedure on a two-layer neural network with shifted ReLU activation, and shows that only $o(m)$ neurons will be activated for each input data per iteration. Furthermore, we develop an algorithm for adversarial training with time cost $o(m n d)$ per iteration by applying half-space reporting data structure. | [] | [] | A Sublinear Adversarial Training Algorithm | [
"Yeqi Gao",
"Lianke Qin",
"Zhao Song",
"Yitan Wang"
] | 2208.05395 | 18,798 | https://openreview.net/forum?id=N2WchST43h |
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[] | Poster | [
"https://github.com/mit-han-lab/streaming-llm"
] | Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses two major challenges.Firstly, during the decoding stage, caching previous tokens' Key and Value states (KV) consumes extensive memory.Secondly, popular LLMs cannot generalize to longer texts than the training sequence length.Window attention, where only the most recent KVs are cached, is a natural approach --- but we show that it fails when the text length surpasses the cache size.We observe an interesting phenomenon, namely attention sink, that keeping the KV of initial tokens will largely recover the performance of window attention. In this paper, we first demonstrate that the emergence of attention sink is due to the strong attention scores towards initial tokens as a ``sink'' even if they are not semantically important.Based on the above analysis, we introduce StreamingLLM, an efficient framework that enables LLMs trained with a finite length attention window to generalize to infinite sequence length without any fine-tuning.We show that StreamingLLM can enable Llama-2, MPT, Falcon, and Pythia to perform stable and efficient language modeling with up to 4 million tokens and more.In addition, we discover that adding a placeholder token as a dedicated attention sink during pre-training can further improve streaming deployment. In streaming settings, StreamingLLM outperforms the sliding window recomputation baseline by up to 22.2$\times$ speedup.Code and datasets are provided in the anonymous link. | [] | [] | Efficient Streaming Language Models with Attention Sinks | [
"Guangxuan Xiao",
"Yuandong Tian",
"Beidi Chen",
"Song Han",
"Mike Lewis"
] | 2309.17453 | 18,794 | https://openreview.net/forum?id=NG7sS51zVF |
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[] | Poster | [] | We contribute to the study of formal languages that can be recognized by transformer encoders. We focus on two self-attention mechanisms: (1) UHAT (Unique Hard Attention Transformers) and (2) AHAT (Average Hard Attention Transformers). UHAT encoders are known to recognize only languages inside the circuit complexity class ${\sf AC}^0$, i.e., accepted by a family of poly-sized and depth-bounded boolean circuits with unbounded fan-ins. On the other hand, AHAT encoders can recognize languages outside ${\sf AC}^0$), but their expressive power still lies within the bigger circuit complexity class ${\sf TC}^0$, i.e., ${\sf AC}^0$-circuits extended by majority gates.We first show a negative result that there is an ${\sf AC}^0$-language that cannot be recognized by an UHAT encoder. On the positive side, we show that UHAT encoders can recognize a rich fragment of ${\sf AC}^0$-languages, namely, all languages definable in first-order logic with arbitrary unary numerical predicates. This logic, includes, for example, all regular languages from ${\sf AC}^0$. We then show that AHAT encoders can recognize all languages of our logic even when we enrich it with counting terms. We apply these results to derive new results on the expressive power of UHAT and AHAT up to permutation of letters (a.k.a. Parikh images). | [] | [] | Logical Languages Accepted by Transformer Encoders with Hard Attention | [
"Pablo Barcelo",
"Alexander Kozachinskiy",
"Anthony Widjaja Lin",
"Vladimir Podolskii"
] | 2310.03817 | 18,141 | https://openreview.net/forum?id=gbrHZq07mq |
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[] | Poster | [] | We formalize and study a phenomenon called *feature collapse* that makes precise the intuitive idea that entities playing a similar role in a learning task receive similar representations. As feature collapse requires a notion of task, we leverage a synthetic task in which a learner must classify `sentences' constituted of $L$ tokens. We start by showing experimentally that feature collapse goes hand in hand with generalization. We then prove that, in the large sample limit, distinct tokens that play identical roles in the task receive identical local feature representations in the first layer of the network. This analysis shows that a neural network trained on this task provably learns interpretable and meaningful representations in its first layer. | [] | [] | Feature Collapse | [
"Thomas Laurent",
"James von Brecht",
"Xavier Bresson"
] | 2305.16162 | 18,140 | https://openreview.net/forum?id=gctmyMiPHH |
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[] | Poster | [] | We present a novel approach named OmniControl for incorporating flexible spatial control signals into a text-conditioned human motion generation model based on the diffusion process. Unlike previous methods that can only control the pelvis trajectory, OmniControl can incorporate flexible spatial control signals over different joints at different times with only one model. Specifically, we propose analytic spatial guidance that ensures the generated motion can tightly conform to the input control signals. At the same time, realism guidance is introduced to refine all the joints to generate more coherent motion. Both the spatial and realism guidance are essential and they are highly complementary for balancing control accuracy and motion realism. By combining them, OmniControl generates motions that are realistic, coherent, and consistent with the spatial constraints. Experiments on HumanML3D and KIT-ML datasets show that OmniControl not only achieves significant improvement over state-of-the-art methods on pelvis control but also shows promising results when incorporating the constraints over other joints. Our code and model weights will be publicly released. | [] | [] | OmniControl: Control Any Joint at Any Time for Human Motion Generation | [
"Yiming Xie",
"Varun Jampani",
"Lei Zhong",
"Deqing Sun",
"Huaizu Jiang"
] | 2310.08580 | 18,139 | https://openreview.net/forum?id=gd0lAEtWso |
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[] | Poster | [] | We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as constraint satisfaction problems and use this framework to investigate how the LLM interacts internally with factual constraints. We find a strong positive relationship between the LLM's attention to constraint tokens and the factual accuracy of generations. We curate a suite of 11 datasets containing over 40,000 prompts to study the task of predicting factual errors with the Llama-2 family across all scales (7B, 13B, 70B). We propose SAT Probe, a method probing attention patterns, that can predict factual errors and fine-grained constraint satisfaction, and allow early error identification. The approach and findings take another step towards using the mechanistic understanding of LLMs to enhance their reliability. | [] | [] | Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models | [
"Mert Yuksekgonul",
"Varun Chandrasekaran",
"Erik Jones",
"Suriya Gunasekar",
"Ranjita Naik",
"Hamid Palangi",
"Ece Kamar",
"Besmira Nushi"
] | 2309.15098 | 18,138 | https://openreview.net/forum?id=gfFVATffPd |
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[] | Poster | [] | Federated Learning (FL) models typically suffer from client drift caused by heterogeneous data, where data distributions vary with clients. To this end, advanced works mainly focus on manipulating exist gradients to obtain more similar client models. In this paper, we propose a different view of client drift and correct it by producing better local models. First, we analyze the generalization contribution of local training and conclude that the generalization contribution of local training is bounded by the conditional Wasserstein distance between clients' data distributions. Then, we propose FedImpro, to constructs similar conditional distributions for local training. Specifically, FedImpro decouples the model into high-level and low-level parts and trains the high-level part on reconstructed feature distributions, causing promoted generalization contribution and alleviated gradient dissimilarity of FL. Experimental results demonstrate that FedImpro can help FL defend against data heterogeneity and improve model generalization | [] | [] | FedImpro: Measuring and Improving Client Update in Federated Learning | [
"Zhenheng Tang",
"Yonggang Zhang",
"Shaohuai Shi",
"Xinmei Tian",
"Tongliang Liu",
"Bo Han",
"Xiaowen Chu"
] | 2402.07011 | 18,137 | https://openreview.net/forum?id=giU9fYGTND |
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[] | Poster | [] | Empowering large language models (LLMs) to accurately express confidence in their answers is essential for reliable and trustworthy decision-making. Previous confidence elicitation methods, which primarily rely on *white-box access* to internal model information or model fine-tuning, have become less suitable for LLMs, especially closed-source commercial APIs. This leads to a growing need to explore the untapped area of *black-box* approaches for LLM uncertainty estimation. To better break down the problem, we define a systematic framework with three components: *prompting* strategies for eliciting verbalized confidence, *sampling* methods for generating multiple responses, and *aggregation* techniques for computing consistency. We then benchmark these methods on two key tasks—confidence calibration and failure prediction—across five types of datasets (e.g., commonsense and arithmetic reasoning) and five widely-used LLMs including GPT-4 and LLaMA 2. Our analysis uncovers several key insights: 1) LLMs, when verbalizing their confidence, tend to be overconfident, potentially imitating human patterns of expressing confidence. 2) As model capability scales up, both calibration and failure prediction performance improve, yet still far from ideal performance. 3) Human-inspired prompting strategies mitigate this overconfidence, albeit with diminishing returns in advanced models like GPT-4, especially in improving failure prediction. 4) Employing sampling strategies paired with specific aggregators can effectively enhance failure prediction; moreover, the choice of aggregator can be tailored based on the desired performance enhancement. Despite these advancements, all investigated methods struggle in challenging tasks, such as those requiring professional knowledge, indicating significant scope for improvement. We believe this study can serve as a strong baseline and provide insights for eliciting confidence in black-box LLMs. | [] | [] | Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs | [
"Miao Xiong",
"Zhiyuan Hu",
"Xinyang Lu",
"YIFEI LI",
"Jie Fu",
"Junxian He",
"Bryan Hooi"
] | 2306.13063 | 18,135 | https://openreview.net/forum?id=gjeQKFxFpZ |
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[] | Poster | [] | Memory-based Dynamic Graph Neural Networks (MDGNNs) are a family of dynamic graph neural networks that leverage a memory module to extract, distill, and memorize long-term temporal dependencies, leading to superior performance compared to memory-less counterparts. However, training MDGNNs faces the challenge of handling entangled temporal and structural dependencies, requiring sequential and chronological processing of data sequences to capture accurate temporal patterns. During the batch training, the temporal data points within the same batch will be processed in parallel, while their temporal dependencies are neglected. This issue is referred to as temporal discontinuity and restricts the effective temporal batch size, limiting data parallelism and reducing MDGNNs' flexibility in industrial applications. This paper studies the efficient training of MDGNNs at scale, focusing on the temporal discontinuity in training MDGNNs with large temporal batch sizes. We first conduct a theoretical study on the impact of temporal batch size on the convergence of MDGNN training. Based on the analysis, we propose PRES, an iterative prediction-correction scheme combined with a memory coherence learning objective to mitigate the effect of temporal discontinuity, enabling MDGNNs to be trained with significantly larger temporal batches without sacrificing generalization performance. Experimental results demonstrate that our approach enables up to a 4 $\times$ larger temporal batch (3.4$\times$ speed-up) during MDGNN training. | [] | [] | PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks | [
"Junwei Su",
"Difan Zou",
"Chuan Wu"
] | 2402.04284 | 18,136 | https://openreview.net/forum?id=gjXor87Xfy |
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[] | Spotlight Poster | [] | Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns about their performance are raised on potential data contamination in their considerable volume of training corpus. Moreover, the static nature and fixed complexity of current benchmarks may inadequately gauge the advancing capabilities of LLMs. In this paper, we introduce DyVal, a novel, general, and flexible evaluation protocol for dynamic evaluation of LLMs. Based on our proposed dynamic evaluation framework, we build graph-informed DyVal by leveraging the structural advantage of directed acyclic graphs to dynamically generate evaluation samples with controllable complexities. DyVal generates challenging evaluation sets on reasoning tasks including mathematics, logical reasoning, and algorithm problems. We evaluate various LLMs ranging from Flan-T5-large to ChatGPT and GPT4. Experiments demonstrate that LLMs perform worse in DyVal-generated evaluation samples with different complexities, emphasizing the significance of dynamic evaluation. We also analyze the failure cases and results of different prompting methods. Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks. We hope that DyVal can shed light on the future evaluation research of LLMs. | [] | [] | DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks | [
"Kaijie Zhu",
"Jiaao Chen",
"Jindong Wang",
"Neil Zhenqiang Gong",
"Diyi Yang",
"Xing Xie"
] | 2309.17167 | 18,134 | https://openreview.net/forum?id=gjfOL9z5Xr |
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[] | Spotlight Poster | [] | Large language models are typically aligned with human preferences by optimizing reward models (RMs) fitted to human feedback. However, human preferences are multi-faceted, and it is increasingly common to derive reward from a composition of simpler reward models which each capture a different aspect of language quality. This itself presents a challenge, as it is difficult to appropriately weight these component RMs when combining them. Compounding this difficulty, because any RM is only a proxy for human evaluation, this process is vulnerable to *overoptimization*, wherein past a certain point, accumulating higher reward is associated with worse human ratings. In this paper, we perform the first study on overoptimization in composite RMs, showing that correlation between component RMs has a significant effect on the locations of these points. We then introduce an approach to solve this issue using constrained reinforcement learning as a means of preventing the agent from exceeding each RM's threshold of usefulness. Our method addresses the problem of weighting component RMs by learning dynamic weights, naturally given by the Lagrange multipliers. As a result, each RM stays within the range at which it is an effective proxy, improving evaluation performance. Finally, we introduce an adaptive method using gradient-free optimization to identify and optimize towards these points during a single run. | [] | [] | Confronting Reward Model Overoptimization with Constrained RLHF | [
"Ted Moskovitz",
"Aaditya K Singh",
"DJ Strouse",
"Tuomas Sandholm",
"Ruslan Salakhutdinov",
"Anca Dragan",
"Stephen Marcus McAleer"
] | 2310.04373 | 18,133 | https://openreview.net/forum?id=gkfUvn0fLU |
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[] | Poster | [] | Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning models on feature-partitioned, distributed data. However, due to privacy restrictions, few public real-world VFL datasets exist for algorithm evaluation, and these represent a limited array of feature distributions. Existing benchmarks often resort to synthetic datasets, derived from arbitrary feature splits from a global set, which only capture a subset of feature distributions, leading to inadequate algorithm performance assessment. This paper addresses these shortcomings by introducing two key factors affecting VFL performance - feature importance and feature correlation - and proposing associated evaluation metrics and dataset splitting methods. Our comprehensive evaluation of cutting-edge VFL algorithms provides valuable insights for future research in the field. | [] | [] | VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks | [
"Zhaomin Wu",
"Junyi Hou",
"Bingsheng He"
] | 2307.02040 | 18,132 | https://openreview.net/forum?id=glwwbaeKm2 |
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[] | Spotlight Poster | [
"https://github.com/OPTML-Group/Unlearn-Saliency"
] | With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models. However, existing MU methods focusing on data and/or weight perspectives often suffer limitations in unlearning accuracy, stability, and cross-domain applicability. To address these challenges, we introduce the concept of 'weight saliency' for MU, drawing parallels with input saliency in model explanation. This innovation directs MU's attention toward specific model weights rather than the entire model, improving effectiveness and efficiency. The resultant method that we call saliency unlearning (SalUn) narrows the performance gap with 'exact' unlearning (model retraining from scratch after removing the forgetting data points). To the best of our knowledge, SalUn is the first principled MU approach that can effectively erase the influence of forgetting data, classes, or concepts in both image classification and generation tasks. As highlighted below, For example, SalUn yields a stability advantage in high-variance random data forgetting, e.g., with a 0.2% gap compared to exact unlearning on the CIFAR-10 dataset. Moreover, in preventing conditional diffusion models from generating harmful images, SalUn achieves nearly 100% unlearning accuracy, outperforming current state-of-the-art baselines like Erased Stable Diffusion and Forget-Me-Not. Codes are available at https://github.com/OPTML-Group/Unlearn-Saliency.**WARNING**: This paper contains model outputs that may be offensive in nature. | [] | [] | SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation | [
"Chongyu Fan",
"Jiancheng Liu",
"Yihua Zhang",
"Eric Wong",
"Dennis Wei",
"Sijia Liu"
] | 2310.12508 | 18,130 | https://openreview.net/forum?id=gn0mIhQGNM |
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[] | Poster | [] | Subgraph GNNs are provably expressive neural architectures that learn graph representations from sets of subgraphs. Unfortunately, their applicability is hampered by the computational complexity associated with performing message passing on many subgraphs. In this paper, we consider the problem of learning to select a small subset of the large set of possible subgraphs in a data-driven fashion. We first motivate the problem by proving that there are families of WL-indistinguishable graphs for which there exist efficient subgraph selection policies: small subsets of subgraphs that can already identify all the graphs within the family. We then propose a new approach, called _Policy-Learn_, that learns how to select subgraphs in an iterative manner. We prove that, unlike popular random policies and prior work addressing the same problem, our architecture is able to learn the efficient policies mentioned above. Our experimental results demonstrate that _Policy-Learn_ outperforms existing baselines across a wide range of datasets. | [] | [] | Efficient Subgraph GNNs by Learning Effective Selection Policies | [
"Beatrice Bevilacqua",
"Moshe Eliasof",
"Eli Meirom",
"Bruno Ribeiro",
"Haggai Maron"
] | 2310.20082 | 18,129 | https://openreview.net/forum?id=gppLqZLQeY |
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[] | Spotlight Poster | [] | Image matching is a fundamental computer vision problem. While learning-based methods achieve state-of-the-art performance on existing benchmarks, they generalize poorly to in-the-wild images. Such methods typically need to train separate models for different scene types (e.g., indoor vs. outdoor) and are impractical when the scene type is unknown in advance. One of the underlying problems is the limited scalability of existing data construction pipelines, which limits the diversity of standard image matching datasets. To address this problem, we propose GIM, a self-training framework for learning a single generalizable model based on any image matching architecture using internet videos, an abundant and diverse data source. Given an architecture, GIM first trains it on standard domain-specific datasets and then combines it with complementary matching methods to create dense labels on nearby frames of novel videos. These labels are filtered by robust fitting, and then enhanced by propagating them to distant frames. The final model is trained on propagated data with strong augmentations. Not relying on complex 3D reconstruction makes GIM much more efficient and less likely to fail than standard SfM-and-MVS based frameworks. We also propose ZEB, the first zero-shot evaluation benchmark for image matching. By mixing data from diverse domains, ZEB can thoroughly assess the cross-domain generalization performance of different methods. Experiments demonstrate the effectiveness and generality of GIM. Applying GIM consistently improves the zero-shot performance of 3 state-of-the-art image matching architectures as the number of downloaded videos increases (Fig. 1 (a)); with 50 hours of YouTube videos, the relative zero-shot performance improves by 8.4% − 18.1%. GIM also enables generalization to extreme cross-domain data such as Bird Eye View (BEV) images of projected 3D point clouds (Fig. 1 (c)). More importantly, our single zero-shot model consistently outperforms domain-specific baselines when evaluated on downstream tasks inherent to their respective domains. The code will be released upon acceptance. | [] | [] | GIM: Learning Generalizable Image Matcher From Internet Videos | [
"Xuelun Shen",
"zhipeng cai",
"Wei Yin",
"Matthias Müller",
"Zijun Li",
"Kaixuan Wang",
"Xiaozhi Chen",
"Cheng Wang"
] | 2402.11095 | 18,782 | https://openreview.net/forum?id=NYN1b8GRGS |
|
[] | Poster | [] | Recent theoretical work has identified surprisingly simple reasoning problems, such as checking if two nodes in a graph are connected or simulating finite-state machines, that are provably unsolvable by standard transformers that answer immediately after reading their input. However, in practice, transformers' reasoning can be improved by allowing them to use a "chain of thought" or "scratchpad", i.e., generate and condition on a sequence of intermediate tokens before answering. Motivated by this, we ask: *Does such intermediate generation fundamentally extend the computational power of a decoder-only transformer?* We show that the answer is *yes*, but the amount of increase depends crucially on the amount of intermediate generation. For instance, we find that transformer decoders with a logarithmic number of decoding steps (w.r.t. the input length) push the limits of standard transformers only slightly, while a linear number of decoding steps adds a clear new ability (under standard complexity conjectures): recognizing all regular languages. Our results also imply that linear steps keep transformer decoders within context-sensitive languages, and polynomial steps make them recognize exactly the class of polynomial-time solvable problems---the first exact characterization of a type of transformers in terms of standard complexity classes. Together, our results provide a nuanced framework for understanding how the length of a transformer’s chain of thought or scratchpad impacts its reasoning power. | [] | [] | The Expressive Power of Transformers with Chain of Thought | [
"William Merrill",
"Ashish Sabharwal"
] | 2310.07923 | 18,776 | https://openreview.net/forum?id=NjNGlPh8Wh |
|
[] | Poster | [] | Current tools for machine learning fairness only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipelines.We introduce a framework of fairness regularization terms (fairret) which quantify bias as modular objectives that are easily integrated in automatic differentiation pipelines. By employing a general definition of fairness in terms of linear-fractional statistics, a wide class of fairrets can be computed efficiently. Experiments show the behavior of their gradients and their utility in enforcing fairness with minimal loss of predictive power compared to baselines. Our contribution includes a PyTorch implementation of the fairret framework. | [] | [] | fairret: a Framework for Differentiable Fairness Regularization Terms | [
"Maarten Buyl",
"MaryBeth Defrance",
"Tijl De Bie"
] | 2310.17256 | 18,770 | https://openreview.net/forum?id=NnyD0Rjx2B |
|
[] | Poster | [
"https://github.com/GAIR-NLP/auto-j"
] | The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address. In the field of Natural Language Processing (NLP), researchers have shifted their focus from conventional NLP tasks (e.g., sequence tagging and parsing) towards tasks that revolve around aligning with human needs (e.g., brainstorming and email writing). This shift in task distribution imposes new requirements on evaluating these aligned models regarding *generality* (i.e., assessing performance across diverse scenarios), *flexibility* (i.e., examining under different protocols), and *interpretability* (i.e., scrutinizing models with explanations). In this paper, we propose a generative judge with 13B parameters, **Auto-J**, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios and accommodates diverse evaluation protocols (e.g., pairwise response comparison and single-response evaluation) with well-structured natural language critiques. To demonstrate the efficacy of our approach, we construct a new testbed covering 58 different scenarios. Experimentally, **Auto-J** outperforms a series of strong competitors, including both open-source and closed-source models, by a large margin. We also provide detailed analysis and case studies to further reveal the potential of our method and make a variety of resources public at https://anonymous.4open.science/r/Auto-J-ICLR-ver-0107. | [] | [] | Generative Judge for Evaluating Alignment | [
"Junlong Li",
"Shichao Sun",
"Weizhe Yuan",
"Run-Ze Fan",
"hai zhao",
"Pengfei Liu"
] | 2310.05470 | 18,128 | https://openreview.net/forum?id=gtkFw6sZGS |
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[] | Poster | [] | Recent empirical evidence indicates that transformer based in-context learning performs better when using a prefix language model (prefixLM), in which in-context samples can all attend to each other, compared to causal language models (causalLM), which use auto-regressive attention that prohibits in-context samples to attend to future samples. While this result is intuitive, it is not understood from a theoretical perspective. In this paper we take a theoretical approach and analyze the convergence behavior of prefixLM and causalLM under a certain parameter construction. Our analysis shows that both LM types converge to their stationary points at a linear rate, but that while prefixLM converges to the optimal solution of linear regression, causalLM convergence dynamics follows that of an online gradient descent algorithm, which is not guaranteed to be optimal even as the number of samples grows infinitely. We supplement our theoretical claims with empirical experiments over synthetic and real tasks and using various types of transformers. Our experiments verify that causalLM consistently underperforms prefixLM in all settings. | [] | [] | CausalLM is not optimal for in-context learning | [
"Nan Ding",
"Tomer Levinboim",
"Jialin Wu",
"Sebastian Goodman",
"Radu Soricut"
] | 2308.06912 | 18,127 | https://openreview.net/forum?id=guRNebwZBb |
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[] | Poster | [] | Restoring facial details from low-quality (LQ) images has remained challenging due to the nature of the problem caused by various degradations in the wild. The codebook prior has been proposed to address the ill-posed problems by leveraging an autoencoder and learned codebook of high-quality (HQ) features, achieving remarkable quality.However, existing approaches in this paradigm frequently depend on a single encoder pre-trained on HQ data for restoring HQ images, disregarding the domain gap and distinct feature representations between LQ and HQ images.As a result, encoding LQ inputs with the same encoder could be insufficient, resulting in imprecise feature representation and leading to suboptimal performance.To tackle this problem, we propose a novel dual-branch framework named $\textit{DAEFR}$. Our method introduces an auxiliary LQ branch that extracts domain-specific information from the LQ inputs. Additionally, we incorporate association training to promote effective synergy between the two branches, enhancing code prediction and restoration quality.We evaluate the effectiveness of DAEFR on both synthetic and real-world datasets, demonstrating its superior performance in restoring facial details.Source codes and trained models will be publicly released. | [] | [] | Dual Associated Encoder for Face Restoration | [
"YU-JU TSAI",
"Yu-Lun Liu",
"Lu Qi",
"Kelvin C.K. Chan",
"Ming-Hsuan Yang"
] | 2308.07314 | 18,126 | https://openreview.net/forum?id=gwDuW7Ok5f |
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[] | Poster | [] | Structure learning is a crucial task in science, especially in fields such as medicine and biology, where the wrong identification of (in)dependencies among random variables can have significant implications. The primary objective of structure learning is to learn a Directed Acyclic Graph (DAG) that represents the underlying probability distribution of the data. Many prominent DAG learners rely on least square losses or log-likelihood losses for optimization. It is well-known from regression models that least square losses are heavily influenced by the scale of the variables. Recently it has been demonstrated that the scale of data also affects performance of structure learning algorithms, though with a strong focus on linear 2-node systems and simulated data. Moving beyond these results, we provide conditions under which square-based losses are minimal for wrong DAGs in $d$-dimensional cases. Furthermore, we also show that scale can impair performance of structure learners if relations among variables are non-linear for both square based and log-likelihood based losses. We confirm our theoretical findings through extensive experiments on synthetic and real-world data. | [] | [] | Learning Large DAGs is Harder than you Think: Many Losses are Minimal for the Wrong DAG | [
"Jonas Seng",
"Matej Zečević",
"Devendra Singh Dhami",
"Kristian Kersting"
] | 18,125 | https://openreview.net/forum?id=gwbQ2YwLhD |
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[] | Poster | [] | Unsupervised video object learning seeks to decompose video scenes into structural object representations without any supervision from depth, optical flow, or segmentation. We present VONet, an innovative approach that is inspired by MONet. While utilizing a U-Net architecture, VONet employs an efficient and effective parallel attention inference process, generating attention masks for all slots simultaneously. Additionally, to enhance the temporal consistency of each mask across consecutive video frames, VONet develops an object-wise sequential VAE framework. The integration of these innovative encoder-side techniques, in conjunction with an expressive transformer-based decoder, establishes VONet as the leading unsupervised method for object learning across five MOVI datasets, encompassing videos of diverse complexities. Code is available at https://github.com/hnyu/vonet. | [] | [] | VONet: Unsupervised Video Object Learning With Parallel U-Net Attention and Object-wise Sequential VAE | [
"Haonan Yu",
"Wei Xu"
] | 17,743 | https://openreview.net/forum?id=qCyhvr0GG8 |
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[] | Poster | [
"https://github.com/zichuan-liu/ContraLSP"
] | Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.Although previous saliency-based methods addressed the challenges,their perturbation may not alleviate the distribution shift issue, which is inevitable especially in heterogeneous samples.We present ContraLSP, a locally sparse model that introduces counterfactual samples to build uninformative perturbations but keeps distribution using contrastive learning.Furthermore, we incorporate sample-specific sparse gates to generate more binary-skewed and smooth masks, which easily integrate temporal trends and select the salient features parsimoniously.Empirical studies on both synthetic and real-world datasets show that ContraLSP outperforms state-of-the-art models, demonstrating a substantial improvement in explanation quality for time series data.The source code is available at \url{https://github.com/zichuan-liu/ContraLSP}. | [] | [] | Explaining Time Series via Contrastive and Locally Sparse Perturbations | [
"Zichuan Liu",
"Yingying ZHANG",
"Tianchun Wang",
"Zefan Wang",
"Dongsheng Luo",
"Mengnan Du",
"Min Wu",
"Yi Wang",
"Chunlin Chen",
"Lunting Fan",
"Qingsong Wen"
] | 2401.08552 | 17,742 | https://openreview.net/forum?id=qDdSRaOiyb |
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[] | Poster | [] | Mesh deformation plays a pivotal role in many 3D vision tasks including dynamic simulations, rendering, and reconstruction. However, defining an efficient discrepancy between predicted and target meshes remains an open problem. A prevalent approach in current deep learning is the set-based approach which measures the discrepancy between two surfaces by comparing two randomly sampled point-clouds from the two meshes with Chamfer pseudo-distance. Nevertheless, the set-based approach still has limitations such as lacking a theoretical guarantee for choosing the number of points in sampled point-clouds, and the pseudo-metricity and the quadratic complexity of the Chamfer divergence. To address these issues, we propose a novel metric for learning mesh deformation. The metric is defined by sliced Wasserstein distance on meshes represented as probability measures that generalize the set-based approach. By leveraging probability measure space, we gain flexibility in encoding meshes using diverse forms of probability measures, such as continuous, empirical, and discrete measures via \textit{varifold} representation. After having encoded probability measures, we can compare meshes by using the sliced Wasserstein distance which is an effective optimal transport distance with linear computational complexity and can provide a fast statistical rate for approximating the surface of meshes. To the end, we employ a neural ordinary differential equation (ODE) to deform the input surface into the target shape by modeling the trajectories of the points on the surface. Our experiments on cortical surface reconstruction demonstrate that our approach surpasses other competing methods in multiple datasets and metrics. | [] | [] | Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction | [
"Thanh Tung Le",
"Khai Nguyen",
"shanlin sun",
"Kun Han",
"Nhat Ho",
"Xiaohui Xie"
] | 2305.17555 | 18,123 | https://openreview.net/forum?id=gxhRR8vUQb |
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[] | Poster | [] | Optimizing and certifying the positivity of polynomials are fundamental primitives across mathematics and engineering applications, from dynamical systems to operations research. However, solving these problems in practice requires large semidefinite programs, with poor scaling in dimension and degree. In this work, we demonstrate for the first time that neural networks can effectively solve such problems in a data-driven fashion, achieving tenfold speedups while retaining high accuracy. Moreover, we observe that these polynomial learning problems are equivariant to the non-compact group $SL(2,\mathbb{R})$, which consists of area-preserving linear transformations. We therefore adapt our learning pipelines to accommodate this structure, including data augmentation, a new $SL(2,\mathbb{R})$-equivariant architecture, and an architecture equivariant with respect to its maximal compact subgroup, $SO(2, \mathbb{R})$. Surprisingly, the most successful approaches in practice do not enforce equivariance to the entire group, which we prove arises from an unusual lack of architecture universality for $SL(2,\mathbb{R})$ in particular. A consequence of this result, which is of independent interest, is that there exists an equivariant function for which there is no sequence of equivariant approximating polynomials. This is a rare example of a symmetric problem where data augmentation outperforms a fully equivariant architecture, and provides interesting lessons in both theory and practice for other problems with non-compact symmetries. | [] | [] | Learning Polynomial Problems with $SL(2, \mathbb{R})$-Equivariance | [
"Hannah Lawrence",
"Mitchell Tong Harris"
] | 18,122 | https://openreview.net/forum?id=gyfXuRfxW2 |
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[] | Poster | [] | In the battle against widespread online misinformation, a growing problem is text-image inconsistency, where images are misleadingly paired with texts with different intent or meaning. Existing classification-based methods for text-image inconsistency can identify contextual inconsistencies but fail to provide explainable justifications for their decisions that humans can understand. Although more nuanced, human evaluation is impractical at scale and susceptible to errors. To address these limitations, this study introduces D-TIIL (Diffusion-based Text-Image Inconsistency Localization), which employs text-to-image diffusion models to localize semantic inconsistencies in text and image pairs. These models, trained on large-scale datasets act as ``omniscient" agents that filter out irrelevant information and incorporate background knowledge to identify inconsistencies. In addition, D-TIIL uses text embeddings and modified image regions to visualize these inconsistencies. To evaluate D-TIIL's efficacy, we introduce a new TIIL dataset containing 14K consistent and inconsistent text-image pairs. Unlike existing datasets, TIIL enables assessment at the level of individual words and image regions and is carefully designed to represent various inconsistencies. D-TIIL offers a scalable and evidence-based approach to identifying and localizing text-image inconsistency, providing a robust framework for future research combating misinformation. | [] | [] | Exposing Text-Image Inconsistency Using Diffusion Models | [
"Mingzhen Huang",
"Shan Jia",
"Zhou Zhou",
"Yan Ju",
"Jialing Cai",
"Siwei Lyu"
] | 2404.18033 | 18,761 | https://openreview.net/forum?id=Ny150AblPu |
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[] | Poster | [
"https://github.com/sail-sg/autofd"
] | We extend JAX with the capability to automatically differentiate higher-order functions (functionals and operators). By representing functions as infinite dimensional generalization of arrays, we seamlessly use JAX's existing primitive system to implement higher-order functions. We present a set of primitive operators that serve as foundational building blocks for constructing several key types of functionals. For every introduced primitive operator, we derive and implement both linearization and transposition rules, aligning with JAX's internal protocols for forward and reverse mode automatic differentiation. This enhancement allows for functional differentiation in the same syntax traditionally use for functions. The resulting functional gradients are themselves functions ready to be invoked in python. We showcase this tool's efficacy and simplicity through applications where functional derivatives are indispensable. | [] | [] | Automatic Functional Differentiation in JAX | [
"Min Lin"
] | 2311.18727 | 18,121 | https://openreview.net/forum?id=gzT61ziSCu |
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[] | Poster | [] | Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust. Path methods are commonly employed to generate rigorous attributions that satisfy three axioms. However, the meaning of attributions remains ambiguous due to distinct path choices. To address the ambiguity, we introduce Concentration Principle, which centrally allocates high attributions to indispensable features, thereby endowing aesthetic and sparsity. We then present SAMP, a model-agnostic interpreter, which efficiently searches the near-optimal path from a pre-defined set of manipulation paths. Moreover, we propose the infinitesimal constraint (IC) and momentum strategy (MS) to improve the rigorousness and optimality. Visualizations show that SAMP can precisely reveal DNNs by pinpointing salient image pixels.We also perform quantitative experiments and observe that our method significantly outperforms the counterparts. | [] | [] | Path Choice Matters for Clear Attributions in Path Methods | [
"Borui Zhang",
"Wenzhao Zheng",
"Jie Zhou",
"Jiwen Lu"
] | 2401.10442 | 18,120 | https://openreview.net/forum?id=gzYgsZgwXa |
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[] | Poster | [] | While Large Language Models (LLMs) are the dominant models for generative tasks in language, they do not perform as well as diffusion models on image and video generation. To effectively use LLMs for visual generation, one crucial component is the visual tokenizer that maps pixel-space inputs to discrete tokens appropriate for LLM learning. In this paper, we introduce \modelname{}, a video tokenizer designed to generate concise and expressive tokens for both videos and images using a common token vocabulary. Equipped with this new tokenizer, we show that LLMs outperform diffusion models on standard image and video generation benchmarks including ImageNet and Kinetics. In addition, we demonstrate that our tokenizer surpasses the previously top-performing video tokenizer on two more tasks: (1) video compression comparable to the next-generation video codec (VCC) according to human evaluations, and (2) learning effective representations for action recognition tasks. | [] | [] | Language Model Beats Diffusion - Tokenizer is key to visual generation | [
"Lijun Yu",
"Jose Lezama",
"Nitesh Bharadwaj Gundavarapu",
"Luca Versari",
"Kihyuk Sohn",
"David Minnen",
"Yong Cheng",
"Agrim Gupta",
"Xiuye Gu",
"Alexander G Hauptmann",
"Boqing Gong",
"Ming-Hsuan Yang",
"Irfan Essa",
"David A Ross",
"Lu Jiang"
] | 2310.05737 | 18,119 | https://openreview.net/forum?id=gzqrANCF4g |
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[] | Poster | [] | As robustness verification methods are becoming more precise, training certifiably robust neural networks is becoming ever more relevant. To this end, certified training methods compute and then optimize an upper bound on the worst-case loss over a robustness specification. Curiously, training methods based on the imprecise interval bound propagation (IBP) consistently outperform those leveraging more precise bounds. Still, we lack a theoretical understanding of the mechanisms making IBP so successful. In this work, we investigate these mechanisms by leveraging a novel metric measuring the tightness of IBP bounds. We first show theoretically that, for deep linear models (DLNs), tightness decreases with width and depth at initialization, but improves with IBP training. We, then, derive sufficient and necessary conditions on weight matrices for IBP bounds to become exact and demonstrate that these impose strong regularization, providing an explanation for the observed robustness-accuracy trade-off. Finally, we show how these results on DLNs transfer to ReLU networks, before conducting an extensive empirical study, (i) confirming this transferability and yielding state-of-the-art certified accuracy, (ii) finding that while all IBP-based training methods lead to high tightness, this increase is dominated by the size of the propagated input regions rather than the robustness specification, and finally (iii) observing that non-IBP-based methods do not increase tightness. Together, these results help explain the success of recent certified training methods and may guide the development of new ones. | [] | [] | Understanding Certified Training with Interval Bound Propagation | [
"Yuhao Mao",
"Mark Niklas Mueller",
"Marc Fischer",
"Martin Vechev"
] | 2306.10426 | 18,118 | https://openreview.net/forum?id=h05eQniJsQ |
|
[] | Poster | [
"https://github.com/VCIP-RGBD/DFormer"
] | We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) Unlike previous works that encode RGB-D information with RGB pretrained backbone, we pretrain the backbone using image-depth pairs from ImageNet-1K, and thus the DFormer is endowed with the capacity to encode RGB-D representations; 2) DFormer comprises a sequence of RGB-D blocks, which are tailored for encoding both RGB and depth information through a novel building block design. DFormer avoids the mismatched encoding of the 3D geometry relationships in depth maps by RGB pretrained backbones, which widely lies in existing methods but has not been resolved. We finetune the pretrained DFormer on two popular RGB-D tasks, i.e., RGB-D semantic segmentation and RGB-D salient object detection, with a lightweight decoder head. Experimental results show that our DFormer achieves new state-of-the-art performance on these two tasks with less than half of the computational cost of the current best methods on two RGB-D semantic segmentation datasets and five RGB-D salient object detection datasets. Code will be made publicly available. | [] | [] | DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation | [
"Bowen Yin",
"Xuying Zhang",
"Zhong-Yu Li",
"Li Liu",
"Ming-Ming Cheng",
"Qibin Hou"
] | 2309.09668 | 18,117 | https://openreview.net/forum?id=h1sFUGlI09 |
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[] | Poster | [] | Recently, a series of papers proposed deep learning-based approaches to sample from unnormalized target densities using controlled diffusion processes. In this work, we identify these approaches as special cases of the Schrödinger bridge problem, seeking the most likely stochastic evolution between a given prior distribution and the specified target, and propose the perspective from measures on path space as a unifying framework. The optimal controls of such entropy-constrained optimal transport problems can then be described by systems of partial differential equations and corresponding backward stochastic differential equations. Building on these optimality conditions and exploiting the path measure perspective, we obtain variational formulations of the respective approaches and recover the objectives which can be approached via gradient descent. Our formulations allow to introduce losses different from the typically employed reverse Kullback-Leibler divergence that is known to suffer from mode collapse. In particular, we propose the so-called $\textit{log-variance loss}$, which exhibits favorable numerical properties and leads to significantly improved performance across all considered approaches. | [] | [] | Improved sampling via learned diffusions | [
"Lorenz Richter",
"Julius Berner"
] | 2307.01198 | 18,116 | https://openreview.net/forum?id=h4pNROsO06 |
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[] | Poster | [] | Dataset distillation methods have achieved remarkable success in distilling a large dataset into a small set of representative samples. However, they are not designed to produce a distilled dataset that can be effectively used for facilitating self-supervised pre-training. To this end, we propose a novel problem of distilling an unlabeled dataset into a set of small synthetic samples for efficient self-supervised learning (SSL). We first prove that a gradient of synthetic samples with respect to a SSL objective in naive bilevel optimization is \textit{biased} due to the randomness originating from data augmentations or masking. To address this issue, we propose to minimize the mean squared error (MSE) between a model's representations of the synthetic examples and their corresponding learnable target feature representations for the inner objective, which does not introduce any randomness. Our primary motivation is that the model obtained by the proposed inner optimization can mimic the \textit{self-supervised target model}. To achieve this, we also introduce the MSE between representations of the inner model and the self-supervised target model on the original full dataset for outer optimization. Lastly, assuming that a feature extractor is fixed, we only optimize a linear head on top of the feature extractor, which allows us to reduce the computational cost and obtain a closed-form solution of the head with kernel ridge regression. We empirically validate the effectiveness of our method on various applications involving transfer learning. | [] | [] | Self-Supervised Dataset Distillation for Transfer Learning | [
"Dong Bok Lee",
"Seanie Lee",
"Joonho Ko",
"Kenji Kawaguchi",
"Juho Lee",
"Sung Ju Hwang"
] | 2310.06511 | 18,115 | https://openreview.net/forum?id=h57gkDO2Yg |
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[] | Poster | [
"https://github.com/YangLing0818/VQGraph"
] | GNN-to-MLP distillation aims to utilize knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (student MLP) on graph data by mimicking the output representations of teacher GNN. Existing methods mainly make the MLP to mimic the GNN predictions over a few class labels. However, the class space may not be expressive enough for covering numerous diverse local graph structures, thus limiting the performance of knowledge transfer from GNN to MLP. To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation. Specifically, we propose a variant of VQ-VAE to learn a structure-aware tokenizer on graph data that can encode each node's local substructure as a discrete code. The discrete codes constitute a codebook as a new graph representation space that is able to identify different local graph structures of nodes with the corresponding code indices. Then, based on the learned codebook, we propose a new distillation target, namely soft code assignments, to directly transfer the structural knowledge of each node from GNN to MLP. The resulting framework VQGraph achieves new state-of-the-art performance on GNN-to-MLP distillation in both transductive and inductive settings across seven graph datasets. We show that VQGraph with better performance infers faster than GNNs by 828×, and also achieves accuracy improvement over GNNs and stand-alone MLPs by 3.90% and 28.05% on average, respectively. Our code is available at https://github.com/YangLing0818/VQGraph | [] | [] | VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs | [
"Ling Yang",
"Ye Tian",
"Minkai Xu",
"Zhongyi Liu",
"Shenda Hong",
"Wei Qu",
"Wentao Zhang",
"Bin CUI",
"Muhan Zhang",
"Jure Leskovec"
] | 2308.02117 | 18,114 | https://openreview.net/forum?id=h6Tz85BqRI |
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[] | Poster | [] | Retrosynthetic planning is a sequential decision-making process of identifying synthetic routes from the available building block materials to reach a desired target molecule.Though existing planning approaches show promisingly high solving rates and low costs, the trivial route cost evaluation via pre-trained forward reaction prediction models certainly falls short of real-world chemical practice.An alternative option is to annotate the actual cost of a route, such as yield, through chemical experiments or input from chemists, while this often leads to substantial query costs.In order to strike the balance between query costs and route quality evaluation, we propose an Active Retrosynthetic Planning (ARP) framework that remains compatible with the established retrosynthetic planners.On one hand, the proposed ARP trains an actor that decides whether to query the cost of a reaction; on the other hand, it resorts to a critic to estimate the value of a molecule with its preceding reaction cost as input. Those molecules with low reaction costs are preferred to expand first.We apply our framework to different existing approaches on both the benchmark and an expert dataset and demonstrate that it outperforms the existing state-of-the-art approach by 6.2\% in route quality while reducing the query cost by 12.8\%.In addition, ARP consistently plans high-quality routes with either abundant or sparse annotations. | [] | [] | Active Retrosynthetic Planning Aware of Route Quality | [
"Luotian Yuan",
"Yemin Yu",
"Ying Wei",
"Yongwei Wang",
"Zhihua Wang",
"Fei Wu"
] | 18,113 | https://openreview.net/forum?id=h7DGnWGeos |
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[] | Spotlight Poster | [] | We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities. We allow a partially observed setting in which each view constitutes a nonlinear mixture of a subset of underlying latent variables, which can be causally related.We prove that the information shared across all subsets of any number of views can be learned up to a smooth bijection using contrastive learning and a single encoder per view. We also provide graphical criteria indicating which latent variables can be identified through a simple set of rules, which we refer to as identifiability algebra. Our general framework and theoretical results unify and extend several previous work on multi-view nonlinear ICA, disentanglement, and causal representation learning. We experimentally validate our claims on numerical, image, and multi-modal data sets. Further, we demonstrate that the performance of prior methods is recovered in different special cases of our setup. Overall, we find that access to multiple partial views offers unique opportunities for identifiable representation learning, enabling the discovery of latent structures from purely observational data. | [] | [] | Multi-View Causal Representation Learning with Partial Observability | [
"Dingling Yao",
"Danru Xu",
"Sebastien Lachapelle",
"Sara Magliacane",
"Perouz Taslakian",
"Georg Martius",
"Julius von Kügelgen",
"Francesco Locatello"
] | 2311.04056 | 18,749 | https://openreview.net/forum?id=OGtnhKQJms |
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[] | Poster | [] | Understanding the 3D structures of protein multimers is crucial, as they play a vital role in regulating various cellular processes. It has been empirically confirmed that the multimer structure prediction (MSP) can be well handled in a step-wise assembly fashion using provided dimer structures and predicted protein-protein interactions (PPIs). However, due to the biological gap in the formation of dimers and larger multimers, directly applying PPI prediction techniques can often cause a poor generalization to the MSP task. To address this challenge, we aim to extend the PPI knowledge to multimers of different scales (i.e., chain numbers). Specifically, we propose PromptMSP, a pre-training and Prompt tuning framework for Multimer Structure Prediction. First, we tailor the source and target tasks for effective PPI knowledge learning and efficient inference, respectively. We design PPI-inspired prompt learning to narrow the gaps of two task formats and generalize the PPI knowledge to multimers of different scales. We utilize the meta-learning approach to learn a reliable initialization of the prompt model, enabling our prompting framework to effectively adapt to limited data for large-scale multimers. Empirically, we achieve both significant accuracy (RMSD and TM-Score) and efficiency improvements compared to advanced MSP models. For instance, when both methods utilize AlphaFold-Multimer to prepare dimers, PromptMSP achieves a 21.43\% improvement in TM-Score with only 0.5\% of the running time compared to the competitive MoLPC baseline. | [] | [] | Protein Multimer Structure Prediction via Prompt Learning | [
"Ziqi Gao",
"Xiangguo Sun",
"Zijing Liu",
"Yu Li",
"Hong Cheng",
"Jia Li"
] | 2402.18813 | 18,748 | https://openreview.net/forum?id=OHpvivXrQr |
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[] | Spotlight Poster | [] | Effectively handling both homogeneous and heterogeneous tasks is crucial for the practical application of cooperative agents. However, existing solutions have not been successful in addressing both types of tasks simultaneously. On one hand, value-decomposition-based approaches demonstrate superior performance in homogeneous tasks. Nevertheless, they tend to produce agents with similar policies, which is unsuitable for heterogeneous tasks. On the other hand, solutions based on personalized observation or assigned roles are well-suited for heterogeneous tasks. However, they often lead to a trade-off situation where the agent's performance in homogeneous scenarios is negatively affected due to the aggregation of distinct policies. An alternative approach is to adopt sequential execution policies, which offer a flexible form for learning both types of tasks. However, learning sequential execution policies poses challenges in terms of credit assignment, and the lack of sufficient information about subsequently executed agents can lead to sub-optimal solutions. To tackle these issues, this paper proposes Greedy Sequential Execution (GSE) as a solution to learn the optimal policy that covers both scenarios. In the proposed GSE framework, we introduce an individual utility function into the framework of value decomposition to consider the complex interactions between agents. This function is capable of representing both the homogeneous and heterogeneous optimal policies. Furthermore, we utilize a greedy marginal contribution calculated by the utility function as the credit value of the sequential execution policy to address the credit assignment problem. We evaluated GSE in both homogeneous and heterogeneous scenarios. The results demonstrate that GSE achieves significant improvement in performance across multiple domains, especially in scenarios involving both homogeneous and heterogeneous tasks. | [] | [] | Solving Homogeneous and Heterogeneous Cooperative Tasks with Greedy Sequential Execution | [
"Shanqi Liu",
"Dong Xing",
"Pengjie Gu",
"Xinrun Wang",
"Bo An",
"Yong Liu"
] | 18,108 | https://openreview.net/forum?id=hB2hXtxIPH |
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[] | Poster | [] | Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable responses. However, the potential of LLMs are not fully harnessed by commonly-used prompting methods: many human-in-the-loop algorithms employ ad-hoc procedures for prompt selection; while auto prompt generation approaches are essentially searching all possible prompts randomly and inefficiently. We propose Evoke, an automatic prompt refinement framework. In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback. Such an author-reviewer feedback loop ensures that the prompt is refined in each iteration. We further aggregate a data selection approach to Evoke, where only the hard samples are exposed to the LLM. The hard samples are more important because the LLM can develop deeper understanding of the tasks out of them, while the model may already know how to solve the easier cases. Experimental results show that Evoke significantly outperforms existing methods. For instance, in the challenging task of logical fallacy detection, Evoke scores above 80, while all other baseline methods struggle to reach 20. | [] | [] | Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing | [
"Xinyu Hu",
"Pengfei Tang",
"Simiao Zuo",
"Zihan Wang",
"Bowen Song",
"Qiang Lou",
"Jian Jiao",
"Denis X Charles"
] | 2310.13855 | 18,739 | https://openreview.net/forum?id=OXv0zQ1umU |
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[] | Spotlight Poster | [] | Unlabeled data is a key component of modern machine learning. In general, the roleof unlabeled data is to impose a form of smoothness, usually from the similarityinformation encoded in a base kernel, such as the ϵ-neighbor kernel or the adjacencymatrix of a graph. This work revisits the classical idea of spectrally transformedkernel regression (STKR), and provides a new class of general and scalable STKRestimators able to leverage unlabeled data. Intuitively, via spectral transformation,STKR exploits the data distribution for which unlabeled data can provide additionalinformation. First, we show that STKR is a principled and general approach,by characterizing a universal type of “target smoothness”, and proving that anysufficiently smooth function can be learned by STKR. Second, we provide scalableSTKR implementations for the inductive setting and a general transformationfunction, while prior work is mostly limited to the transductive setting. Third, wederive statistical guarantees for two scenarios: STKR with a known polynomialtransformation, and STKR with kernel PCA when the transformation is unknown.Overall, we believe that this work helps deepen our understanding of how to workwith unlabeled data, and its generality makes it easier to inspire new methods. | [] | [] | Spectrally Transformed Kernel Regression | [
"Runtian Zhai",
"Rattana Pukdee",
"Roger Jin",
"Maria Florina Balcan",
"Pradeep Kumar Ravikumar"
] | 2402.00645 | 18,734 | https://openreview.net/forum?id=OeQE9zsztS |
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[] | Spotlight Poster | [] | We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. Due to the high-dimensionality of humanoid control as well as the inherent difficulties in reinforcement learning, prior methods have focused on learning skill embeddings for a narrow range of movement styles (e.g. locomotion, game characters) from specialized motion datasets. This limited scope hampers its applicability in complex tasks. Our work closes this gap, significantly increasing the coverage of motion representation space. To achieve this, we first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator. This is achieved using an encoder-decoder structure with a variational information bottleneck. Additionally, we jointly learn a prior conditioned on proprioception (humanoid's own pose and velocities) to improve model expressiveness and sampling efficiency for downstream tasks. Sampling from the prior, we can generate long, stable, and diverse human motions. Using this latent space for hierarchical RL, we show that our policies solve tasks using natural and realistic human behavior. We demonstrate the effectiveness of our motion representation by solving generative tasks and motion tracking using VR controllers. | [] | [] | Universal Humanoid Motion Representations for Physics-Based Control | [
"Zhengyi Luo",
"Jinkun Cao",
"Josh Merel",
"Alexander Winkler",
"Jing Huang",
"Kris M. Kitani",
"Weipeng Xu"
] | 2310.04582 | 18,728 | https://openreview.net/forum?id=OrOd8PxOO2 |
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[] | Poster | [
"https://github.com/zmy1116/phylogfn"
] | Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history and numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods. | [] | [] | PhyloGFN: Phylogenetic inference with generative flow networks | [
"Ming Yang Zhou",
"Zichao Yan",
"Elliot Layne",
"Nikolay Malkin",
"Dinghuai Zhang",
"Moksh Jain",
"Mathieu Blanchette",
"Yoshua Bengio"
] | 2310.08774 | 18,107 | https://openreview.net/forum?id=hB7SlfEmze |
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[] | Poster | [] | With the huge success of GPT models in natural language processing, there is a growing interest in applying language modeling approaches to speech tasks.Currently, the dominant architecture in speech-to-speech translation (S2ST) remains the encoder-decoder paradigm, creating a need to investigate the impact of language modeling approaches in this area. In this study, we introduce PolyVoice, a language model-based framework designed for S2ST systems. Our framework comprises three decoder-only language models: a translation language model, a duration language model, and a speech synthesis language model. These language models employ different types of prompts to extract learned information effectively. By utilizing unsupervised semantic units, our framework can transfer semantic information across these models, making it applicable even to unwritten languages. We evaluate our system on Chinese $\rightarrow$ English and English $\rightarrow$ Spanish language pairs. Experimental results demonstrate that PolyVoice outperforms the state-of-the-art encoder-decoder model, producing voice-cloned speech with high translation and audio quality.Speech samples are available at \url{https://polyvoice.github.io}. | [] | [] | PolyVoice: Language Models for Speech to Speech Translation | [
"Qian qian Dong",
"Zhiying Huang",
"Qiao Tian",
"Chen Xu",
"Tom Ko",
"yunlong zhao",
"Siyuan Feng",
"Tang Li",
"Kexin Wang",
"Xuxin Cheng",
"Fengpeng Yue",
"Ye Bai",
"Xi Chen",
"Lu Lu",
"Zejun MA",
"Yuping Wang",
"Mingxuan Wang",
"Yuxuan Wang"
] | 2306.02982 | 18,106 | https://openreview.net/forum?id=hCrFG9cyuC |
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[] | Poster | [] | Deep clustering, which learns representation and semantic clustering without labels information, poses a great challenge for deep learning-based approaches. Despite significant progress in recent years, most existing methods focus on uniformly distributed datasets, significantly limiting the practical applicability of their methods. In this paper, we first introduce a more practical problem setting named deep imbalanced clustering, where the underlying classes exhibit an imbalance distribution. To tackle this problem, we propose a novel pseudo-labeling-based learning framework. Our framework formulates pseudo-label generation as a progressive partial optimal transport problem, which progressively transports each sample to imbalanced clusters under prior distribution constraints, thus generating imbalance-aware pseudo-labels and learning from high-confidence samples.In addition, we transform the initial formulation into an unbalanced optimal transport problem with augmented constraints, which can be solved efficiently by a fast matrix scaling algorithm. Experiments on various datasets, including a human-curated long-tailed CIFAR100, challenging ImageNet-R, and large-scale subsets of fine-grained iNaturalist2018 datasets, demonstrate the superiority of our method. | [] | [] | P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering | [
"Chuyu Zhang",
"Hui Ren",
"Xuming He"
] | 2401.09266 | 18,105 | https://openreview.net/forum?id=hD3sGVqPsr |
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[] | Poster | [] | In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently processing structural data and suffer from high inference costs. In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs while mitigating their limitations. Specifically, LLMs are leveraged to annotate a small portion of nodes and then GNNs are trained on LLMs' annotations to make predictions for the remaining large portion of nodes. The implementation of LLM-GNN faces a unique challenge: how can we actively select nodes for LLMs to annotate and consequently enhance the GNN training? How can we leverage LLMs to obtain annotations of high quality, representativeness, and diversity, thereby enhancing GNN performance with less cost?To tackle this challenge, we develop an annotation quality heuristic and leverage the confidence scores derived from LLMs to advanced node selection. Comprehensive experimental results validate the effectiveness of LLM-GNN. In particular, LLM-GNN can achieve an accuracy of 74.9\% on a vast-scale dataset \products with a cost less than 1 dollar. | [] | [] | Label-free Node Classification on Graphs with Large Language Models (LLMs) | [
"Zhikai Chen",
"Haitao Mao",
"Hongzhi Wen",
"Haoyu Han",
"Wei Jin",
"Haiyang Zhang",
"Hui Liu",
"Jiliang Tang"
] | 2310.04668 | 18,104 | https://openreview.net/forum?id=hESD2NJFg8 |
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[] | Poster | [] | Despite the impressive performance across numerous tasks, large language models (LLMs) often fail in solving simple decision-making tasks due to the misalignment of the knowledge in LLMs with environments. On the contrary, reinforcement learning (RL) agents learn policies from scratch, which makes them always align with environments but difficult to incorporate prior knowledge for efficient explorations. To narrow the gap, we propose TWOSOME, a novel general online framework that deploys LLMs as decision-making agents to efficiently interact and align with embodied environments via RL without requiring any prepared datasets or prior knowledge of the environments. Firstly, we query thejoint probabilities of each valid action with LLMs to form behavior policies. Then, to enhance the stability and robustness of the policies, we propose two normalization methods and summarize four prompt design principles. Finally, we design a novel parameter-efficient training architecture where the actor and critic share one frozen LLM equipped with low-rank adapters (LoRA) updated by PPO. We conduct extensive experiments to evaluate TWOSOME. i) TWOSOME exhibits significantly better sample efficiency and performance compared to the conventional RL method, PPO, and prompt tuning method, SayCan, in both classical decision-making environment, Overcooked, and simulated household environment, VirtualHome. ii) Benefiting from LLMs' open-vocabulary feature, TWOSOME shows superior generalization ability to unseen tasks. iii) Under our framework, there is no significant loss of the LLMs' original ability during online PPO finetuning. | [] | [] | True Knowledge Comes from Practice: Aligning Large Language Models with Embodied Environments via Reinforcement Learning | [
"Weihao Tan",
"Wentao Zhang",
"Shanqi Liu",
"Longtao Zheng",
"Xinrun Wang",
"Bo An"
] | 18,102 | https://openreview.net/forum?id=hILVmJ4Uvu |
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[] | Poster | [
"https://github.com/yifanzhang-pro/Kernel-InfoNCE"
] | Contrastive learning is a powerful self-supervised learning method, but we have a limited theoretical understanding of how it works and why it works. In this paper, we prove that contrastive learning with the standard InfoNCE loss is equivalent to spectral clustering on the similarity graph. Using this equivalence as the building block, we extend our analysis to the CLIP model and rigorously characterize how similar multi-modal objects are embedded together. Motivated by our theoretical insights, we introduce the Kernel-InfoNCE loss, incorporating mixtures of kernel functions that outperform the standard Gaussian kernel on several vision datasets. | [] | [] | Contrastive Learning is Spectral Clustering on Similarity Graph | [
"Zhiquan Tan",
"Yifan Zhang",
"Jingqin Yang",
"Yang Yuan"
] | 2303.15103 | 18,101 | https://openreview.net/forum?id=hLZQTFGToA |
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[] | Spotlight Poster | [
"https://github.com/OpenLemur/Lemur"
] | We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents. The evolution from language chat models to fully functional language agents necessitates models to ground natural language instructions effectively in diverse environments and execute valid actions within them, requiring models for the synergy between language and coding capabilities. Lemur and Lemur-Chat are proposed to address this necessity, demonstrating balanced proficiencies in both domains, unlike existing open-source models that tend to specialize in either. Through meticulous pre-training using a code-intensive corpus and instruction fine-tuning on text and code data, our models achieve state-of-the-art averaged performance across diverse text and coding benchmarks. Comprehensive experiments demonstrate Lemur’s superiority over existing open-source models and its proficiency across various agent tasks involving human communication, tool usage, and interaction under fully- and partially- observable environments. The harmonization between natural and programming languages enables Lemur-Chat to significantly narrow the gap with proprietary models on agent abilities, providing key insights into developing advanced open-source agents adept at reasoning, planning, and operating seamlessly across environments. Our model and code will be open-sourced. | [] | [] | Lemur: Harmonizing Natural Language and Code for Language Agents | [
"Yiheng Xu",
"Hongjin SU",
"Chen Xing",
"Boyu Mi",
"Qian Liu",
"Weijia Shi",
"Binyuan Hui",
"Fan Zhou",
"Yitao Liu",
"Tianbao Xie",
"Zhoujun Cheng",
"Siheng Zhao",
"Lingpeng Kong",
"Bailin Wang",
"Caiming Xiong",
"Tao Yu"
] | 2310.06830 | 18,100 | https://openreview.net/forum?id=hNhwSmtXRh |
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[] | Poster | [] | Learning inherently interpretable policies is a central challenge in the path to developing autonomous agents that humans can trust. Linear policies can justify their decisions while interacting in a dynamic environment, but their reduced expressivity prevents them from solving hard tasks. Instead, we argue for the use of piecewise-linear policies. We carefully study to what extent they can retain the interpretable properties of linear policies while reaching competitive performance with neural baselines. In particular, we propose the HyperCombinator (HC), a piecewise-linear neural architecture expressing a policy with a controllably small number of sub-policies. Each sub-policy is linear with respect to interpretable features, shedding light on the decision process of the agent without requiring an additional explanation model. We evaluate HC policies in control and navigation experiments, visualize the improved interpretability of the agent and highlight its trade-off with performance. Moreover, we validate that the restricted model class that the HyperCombinator belongs to is compatible with the algorithmic constraints of various reinforcement learning algorithms. | [] | [] | Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning | [
"Maxime Wabartha",
"Joelle Pineau"
] | 18,099 | https://openreview.net/forum?id=hOMVq57Ce0 |
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[] | Poster | [] | In nature, the behavior of many complex systems can be described by parsimonious math equations. Symbolic Regression (SR) is defined as the task of automatically distilling equations from limited data. Keen efforts have been placed on tackling this issue and demonstrated success in SR. However, there still exist bottlenecks that current methods struggle to break, when the expressions we need to explore tend toward infinity and especially when the underlying math formula is intricate. To this end, we propose a novel Reinforcement Symbolic Regression Machine (RSRM) that masters the capability of uncovering complex math equations from only scarce data. The RSRM model is composed of three key modules: (1) a Monte Carlo tree search (MCTS) agent, designed for exploration, that explores optimal math expression trees consisting of pre-defined math operators and variables, (2) a Double Q-learning block, designed for exploitation, that helps reduce the feasible search space of MCTS via properly understanding the distribution of reward, and (3) a modulated sub-tree discovery block that heuristically learns and defines new math operators to improve representation ability of math expression trees. Binding of these modules yields the SOTA performance of RSRM in SR as demonstrated by multiple benchmark datasets. The RSRM shows clear superiority over several representative baseline models. | [] | [] | Reinforcement Symbolic Regression Machine | [
"Yilong Xu",
"Yang Liu",
"Hao Sun"
] | 2305.14656 | 18,713 | https://openreview.net/forum?id=PJVUWpPnZC |
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[] | Poster | [] | This paper studies the challenging problem of estimating causal effects from observational data, in the presence of unobserved confounders. The two-stage least square (TSLS) method and its variants with a standard instrumental variable (IV) are commonly used to eliminate confounding bias, including the bias caused by unobserved confounders, but they rely on the linearity assumption. Besides, the strict condition of unconfounded instruments posed on a standard IV is too strong to be practical. To address these challenging and practical problems of the standard IV method (linearity assumption and the strict condition), in this paper, we use a conditional IV (CIV) to relax the unconfounded instrument condition of standard IV and propose a non-linear \underline{CIV} regression with \underline{C}onfounding \underline{B}alancing \underline{R}epresentation \underline{L}earning, CBRL.CIV, for jointly eliminating the confounding bias from unobserved confounders and balancing the observed confounders, without the linearity assumption. We theoretically demonstrate the soundness of CBRL.CIV. Extensive experiments on synthetic and two real-world datasets show the competitive performance of CBRL.CIV against state-of-the-art IV-based estimators and superiority in dealing with the non-linear situation. | [] | [] | Conditional Instrumental Variable Regression with Representation Learning for Causal Inference | [
"Debo Cheng",
"Ziqi Xu",
"Jiuyong Li",
"Lin Liu",
"Jixue Liu",
"Thuc Duy Le"
] | 2310.01865 | 17,741 | https://openreview.net/forum?id=qDhq1icpO8 |
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[] | Poster | [] | Despite the recent advancement in deep reinforcement learning (DRL), it still struggles at learning sparse-reward goal-directed tasks. On the other hand, classical planning approaches excel at addressing tasks with hierarchical structures by employing symbolic knowledge for high-level planning. Yet, most classical planning methods rely on assumptions about pre-defined subtasks, making them inapplicable in domains without domain knowledge or models. To bridge the best of both worlds, we propose a framework that integrates DRL with classical planning by automatically inducing task structures and substructures from a few demonstrations. Specifically, we use symbolic regression for substructure induction by adopting genetic programming where the program model reflects prior domain knowledge of effect rules. We compare our proposed framework to DRL algorithms, imitation learning methods, and an exploration approach in various domains. The experimental results show that our framework outperforms the baselines in terms of sample efficiency and task performance. Moreover, our framework achieves strong generalization performance by inducing the new rules and composing the task structures. Ablation studies justify the design of the induction module and the proposed genetic programming procedure. | [] | [] | Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures | [
"Jung-Chun Liu",
"Chi-Hsien Chang",
"Shao-Hua Sun",
"Tian-Li Yu"
] | 18,705 | https://openreview.net/forum?id=PR6RMsxuW7 |
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[] | Poster | [] | Distributed and federated learning algorithms and techniques associated primarily with minimization problems. However, with the increase of minimax optimization and variational inequality problems in machine learning, the necessity of designing efficient distributed/federated learning approaches for these problems is becoming more apparent. In this paper, we provide a unified convergence analysis of communication-efficient local training methods for distributed variational inequality problems (VIPs). Our approach is based on a general key assumption on the stochastic estimates that allows us to propose and analyze several novel local training algorithms under a single framework for solving a class of structured non-monotone VIPs. We present the first local gradient descent-accent algorithms with provable improved communication complexity for solving distributed variational inequalities on heterogeneous data. The general algorithmic framework recovers state-of-the-art algorithms and their sharp convergence guarantees when the setting is specialized to minimization or minimax optimization problems. Finally, we demonstrate the strong performance of the proposed algorithms compared to state-of-the-art methods when solving federated minimax optimization problems. | [] | [] | Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates | [
"Siqi Zhang",
"Sayantan Choudhury",
"Sebastian U Stich",
"Nicolas Loizou"
] | 2306.05100 | 18,098 | https://openreview.net/forum?id=hORCalGn3Z |
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[] | Poster | [] | We revisit the problem of sampling from a target distribution that has a smooth strongly log-concave density everywhere in $\mathbb{R}^p$. In this context, if no additional density information is available, the randomized midpoint discretization for the kinetic Langevin diffusion is known to be the most scalable method in high dimensions with large condition numbers. Our main result is a nonasymptotic and easy to compute upper bound on the $W_2$-error of this method. To provide a more thorough explanation of our method for establishing the computable upper bound, we conduct an analysis of the midpoint discretization for the vanilla Langevin process. This analysis helps to clarify the underlying principles and provides valuable insights that we use to establish an improved upper bound for the kinetic Langevin process with the midpoint discretization. Furthermore, by applying these techniques we establish new guarantees for the kinetic Langevin process with Euler discretization, which have a better dependence on the condition number than existing upper bounds | [] | [] | Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited | [
"Lu Yu",
"Avetik Karagulyan",
"Arnak S. Dalalyan"
] | 2306.08494 | 18,097 | https://openreview.net/forum?id=hOxgrGM63n |
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[] | Poster | [] | Training AI models that generalize across tasks and domains has long been among the open problems driving AI research. The emergence of Foundation Models made it easier to obtain expert models for a given task, but the heterogeneity of data that may be encountered at test time often means that any single expert is insufficient. We consider the Fusion of Experts (FoE) problem of fusing outputs of expert models with complementary knowledge of the data distribution and formulate it as an instance of supervised learning. Our method is applicable to both discriminative and generative tasks and leads to significant performance improvements in image and text classification, text summarization, multiple-choice QA, and automatic evaluation of generated text. We also extend our method to the "frugal" setting where it is desired to reduce the number of expert model evaluations at test time. | [] | [] | Fusing Models with Complementary Expertise | [
"Hongyi Wang",
"Felipe Maia Polo",
"Yuekai Sun",
"Souvik Kundu",
"Eric Xing",
"Mikhail Yurochkin"
] | 2310.01542 | 18,695 | https://openreview.net/forum?id=PhMrGCMIRL |
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[] | Poster | [] | We introduce $\textbf{Bongard-OpenWorld}$, a new benchmark for evaluating real-world few-shot reasoning for machine vision. It originates from the classical Bongard Problems (BPs): Given two sets of images (positive and negative), the model needs to identify the set that query images belong to by inducing the visual concept, which is exclusively depicted by images from the positive set. Our benchmark inherits the few-shot concept induction of the original BPs while adding the two novel layers of challenge: 1) open-world free-form concepts, as the visual concepts in Bongard-OpenWorld are unique compositions of terms from an open vocabulary, ranging from object categories to abstract visual attributes and commonsense factual knowledge; 2) real-world images, as opposed to the synthetic diagrams used by many counterparts. In our exploration, Bongard-OpenWorld already imposes a significant challenge to current few-shot reasoning algorithms. We further investigate to which extent the recently introduced Large Language Models (LLMs) and Vision-Language Models (VLMs) can solve our task, by directly probing VLMs, and combining VLMs and LLMs in an interactive reasoning scheme. We even designed a neuro-symbolic reasoning approach that reconciles LLMs & VLMs with logical reasoning to emulate the human problem-solving process for Bongard problems. However, none of these approaches manage to close the human-machine gap, as the best learner achieves 64% accuracy while human participants easily reach 91%. We hope Bongard-OpenWorld can help us better understand the limitations of current visual intelligence and facilitate future research on visual agents with stronger few-shot visual reasoning capabilities. All implementation details and reproduction code, including Bongard-OpenWorld dataset, are available in an anonymous github repository https://github.com/Bongard-OpenWorld. | [] | [] | Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World | [
"Rujie Wu",
"Xiaojian Ma",
"Zhenliang Zhang",
"Wei Wang",
"Qing Li",
"Song-Chun Zhu",
"Yizhou Wang"
] | 18,093 | https://openreview.net/forum?id=hWS4MueyzC |
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[] | Poster | [] | The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client. This paper pays particular attention to the issue of client-side model heterogeneity, a pervasive challenge in the practical implementation of FL that escalates its complexity. Assuming a scenario where each client possesses varied memory storage, processing capabilities and network bandwidth - a phenomenon referred to as system heterogeneity - there is a pressing need to customize a unique model for each client. In response to this, we present an effective and adaptable federated framework FedP3, representing Federated Personalized and Privacy-friendly network Pruning, tailored for model heterogeneity scenarios. Our proposed methodology can incorporate and adapt well-established techniques to its specific instances. | [] | [] | FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity | [
"Kai Yi",
"Nidham Gazagnadou",
"Peter Richtárik",
"Lingjuan Lyu"
] | 2404.09816 | 18,092 | https://openreview.net/forum?id=hbHwZYqk9T |
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[] | Poster | [] | We propose Hyper-Dimensional Function Encoding (HDFE). Given samples of a continuous object (e.g. a function), HDFE produces an explicit vector representation of the given object, invariant to the sample distribution and density. Sample distribution and density invariance enables HDFE to consistently encode continuous objects regardless of their sampling, and therefore allows neural networks to receive continuous objects as inputs for machine learning tasks, such as classification and regression. Besides, HDFE does not require any training and is proved to map the object into an organized embedding space, which facilitates the training of the downstream tasks. In addition, the encoding is decodable, which enables neural networks to regress continuous objects by regressing their encodings. Therefore, HDFE serves as an interface for processing continuous objects. We apply HDFE to function-to-function mapping, where vanilla HDFE achieves competitive performance with the state-of-the-art algorithm. We apply HDFE to point cloud surface normal estimation, where a simple replacement from PointNet to HDFE leads to 12\% and 15\% error reductions in two benchmarks. In addition, by integrating HDFE into the PointNet-based SOTA network, we improve the SOTA baseline by 2.5\% and 1.7\% on the same benchmarks. | [] | [] | Decodable and Sample Invariant Continuous Object Encoder | [
"Dehao Yuan",
"Furong Huang",
"Cornelia Fermuller",
"Yiannis Aloimonos"
] | 2311.00187 | 18,679 | https://openreview.net/forum?id=QLoepRnoue |
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[] | Poster | [] | Head avatar reconstruction, crucial for applications in virtual reality, online meetings, gaming, and film industries, has garnered substantial attention within the computer vision community. The fundamental objective of this field is to faithfully recreate the head avatar and precisely control expressions and postures. Existing methods, categorized into 2D-based warping, mesh-based, and neural rendering approaches, present challenges in maintaining multi-view consistency, incorporating non-facial information, and generalizing to new identities. In this paper, we propose a framework named GPAvatar that reconstructs 3D head avatars from one or several images in a single forward pass. The key idea of this work is to introduce a dynamic point-based expression field driven by a point cloud to precisely and effectively capture expressions. Furthermore, we use a Multi Tri-planes Attention (MTA) fusion module in tri-planes canonical field to leverage information from multiple input images. The proposed method achieves faithful identity reconstruction, precise expression control, and multi-view consistency, demonstrating promising results for free-viewpoint rendering and novel view synthesis. | [] | [] | GPAvatar: Generalizable and Precise Head Avatar from Image(s) | [
"Xuangeng Chu",
"Yu Li",
"Ailing Zeng",
"Tianyu Yang",
"Lijian Lin",
"Yunfei Liu",
"Tatsuya Harada"
] | 2401.10215 | 18,090 | https://openreview.net/forum?id=hgehGq2bDv |
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[] | Spotlight Poster | [] | This work introduces the first toolkit around path-norms that is fully able to encompass general DAG ReLU networks with biases, skip connections and max pooling.This toolkit notably allows us to establish generalization bounds for real modern neural networks that are not only the most widely applicable path-norm based ones, but also recover or beat the sharpest known bounds of this type. These extended path-norms further enjoy the usual benefits of path-norms: ease of computation, invariance under the symmetries of the network, and improved sharpness on feedforward networks compared to the product of operators' norms, another complexity measure most commonly used. The versatility of the toolkit and its ease of implementation allow us to challenge the concrete promises of path-norm-based generalization bounds, by numerically evaluating the sharpest known bounds for ResNets on ImageNet. | [] | [] | A path-norm toolkit for modern networks: consequences, promises and challenges | [
"Antoine Gonon",
"Nicolas Brisebarre",
"Elisa Riccietti",
"Rémi Gribonval"
] | 2310.01225 | 18,089 | https://openreview.net/forum?id=hiHZVUIYik |
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[] | Poster | [
"https://github.com/Peiyannn/MM-PDE.git"
] | Recently, neural networks have been extensively employed to solve partial differential equations (PDEs) in physical system modeling. While major studies focus on learning system evolution on predefined static mesh discretizations, some methods utilize reinforcement learning or supervised learning techniques to create adaptive and dynamic meshes, due to the dynamic nature of these systems. However, these approaches face two primary challenges: (1) the need for expensive optimal mesh data, and (2) the change of the solution space's degree of freedom and topology during mesh refinement. To address these challenges, this paper proposes a neural PDE solver with a neural mesh adapter. To begin with, we introduce a novel data-free neural mesh adaptor, called Data-free Mesh Mover (DMM), with two main innovations. Firstly, it is an operator that maps the solution to adaptive meshes and is trained using the Monge-Ampère equation without optimal mesh data. Secondly, it dynamically changes the mesh by moving existing nodes rather than adding or deleting nodes and edges. Theoretical analysis shows that meshes generated by DMM have the lowest interpolation error bound. Based on DMM, to efficiently and accurately model dynamic systems, we develop a moving mesh based neural PDE solver (MM-PDE) that embeds the moving mesh with a two-branch architecture and a learnable interpolation framework to preserve information within the data. Empirical experiments demonstrate that our method generates suitable meshes and considerably enhances accuracy when modeling widely considered PDE systems. The code can be found at: https://github.com/Peiyannn/MM-PDE.git. | [] | [] | Better Neural PDE Solvers Through Data-Free Mesh Movers | [
"Peiyan Hu",
"Yue Wang",
"Zhi-Ming Ma"
] | 2312.05583 | 18,088 | https://openreview.net/forum?id=hj9ZuNimRl |
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[] | Poster | [] | We study differentially private (DP) algorithms for recovering clusters in well-clustered graphs, which are graphs whose vertex set can be partitioned into a small number of sets, each inducing a subgraph of high inner conductance and small outer conductance. Such graphs have widespread application as a benchmark in the theoretical analysis of spectral clustering.We provide an efficient ($\epsilon$,$\delta$)-DP algorithm tailored specifically for such graphs. Our algorithm draws inspiration from the recent work of Chen et al., who developed DP algorithms for recovery of stochastic block models in cases where the graph comprises exactly two nearly-balanced clusters. Our algorithm works for well-clustered graphs with $k$ nearly-balanced clusters, and the misclassification ratio almost matches the one of the best-known non-private algorithms. We conduct experimental evaluations on datasets with known ground truth clusters to substantiate the prowess of our algorithm. We also show that any (pure) $\epsilon$-DP algorithm would result in substantial error. | [] | [] | A Differentially Private Clustering Algorithm for Well-Clustered Graphs | [
"Weiqiang He",
"Hendrik Fichtenberger",
"Pan Peng"
] | 2403.14332 | 18,087 | https://openreview.net/forum?id=hkSjjs4o5d |
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[] | Poster | [] | Graph transformers (GTs) have emerged as a promising architecture that is theoretically more expressive than message-passing graph neural networks (GNNs). However, typical GT models have at least quadratic complexity and thus cannot scale to large graphs. While there are several linear GTs recently proposed, they still lag behind GNN counterparts on several popular graph datasets, which poses a critical concern on their practical expressivity. To balance the trade-off between expressivity and scalability of GTs, we propose Polynormer, a polynomial-expressive GT model with linear complexity. Polynormer is built upon a novel base model that learns a high-degree polynomial on input features. To enable the base model permutation equivariant, we integrate it with graph topology and node features separately, resulting in local and global equivariant attention models. Consequently, Polynormer adopts a linear local-to-global attention scheme to learn high-degree equivariant polynomials whose coefficients are controlled by attention scores. Polynormer has been evaluated on $13$ homophilic and heterophilic datasets, including large graphs with millions of nodes. Our extensive experiment results show that Polynormer outperforms state-of-the-art GNN and GT baselines on most datasets, even without the use of nonlinear activation functions. | [] | [] | Polynormer: Polynomial-Expressive Graph Transformer in Linear Time | [
"Chenhui Deng",
"Zichao Yue",
"Zhiru Zhang"
] | 2403.01232 | 18,086 | https://openreview.net/forum?id=hmv1LpNfXa |
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[] | Poster | [] | A pervasive challenge in Reinforcement Learning (RL) is the ``curse of dimensionality'' which is the exponential growth in the state-action space when optimizing a high-dimensional target task (Bellman, 95). The framework of curriculum learning trains the agent in a curriculum composed of a sequence of related and more manageable source tasks. The expectation is that when some optimal decision rules are shared across source tasks and the target task, the agent could more quickly pick up the necessary skills to behave optimally in the environment, thus accelerating the learning process. However, this critical assumption of invariant optimal decision rules does not necessarily hold in many practical applications, specifically when the underlying environment contains unobserved confounders. This paper studies the problem of curriculum RL through causal lenses. We derive a sufficient graphical condition characterizing causally aligned source tasks, i.e., the invariance of optimal decision rules holds. We further develop an efficient algorithm to generate a causally aligned curriculum, provided with qualitative causal knowledge of the target environment. Finally, we validate our proposed methodology through experiments in confounded environments. | [] | [] | Causally Aligned Curriculum Learning | [
"Mingxuan Li",
"Junzhe Zhang",
"Elias Bareinboim"
] | 18,083 | https://openreview.net/forum?id=hp4yOjhwTs |
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[] | Poster | [] | We propose InstructDET, a data-centric method for referring object detection (ROD) that localizes target objects based on user instructions. While deriving from referring expressions (REC), the instructions we leverage are greatly diversified to encompass common user intentions related to object detection. For one image, we produce tremendous instructions that refer to every single object and different combinations of multiple objects. Each instruction and its corresponding object bounding boxes (bbxs) constitute one training data pair. In order to encompass common detection expressions, we involve emerging vision-language model (VLM) and large language model (LLM) to generate instructions guided by text prompts and object bbxs, as the generalizations of foundation models are effective to produce human-like expressions (e.g., describing object property, category, and relationship). We name our constructed dataset as InDET. It contains images, bbxs and generalized instructions that are from foundation models. Our InDET is developed from existing REC datasets and object detection datasets, with the expanding potential that any image with object bbxs can be incorporated through using our InstructDET method. By using our InDET dataset, we show that a conventional ROD model surpasses existing methods on standard REC datasets and our InDET test set. Our data-centric method InstructDET, with automatic data expansion by leveraging foundation models, directs a promising field that ROD can be greatly diversified to execute common object detection instructions. | [] | [] | InstructDET: Diversifying Referring Object Detection with Generalized Instructions | [
"Ronghao Dang",
"Jiangyan Feng",
"Haodong Zhang",
"Chongjian GE",
"Lin Song",
"Lijun GONG",
"Chengju Liu",
"Qijun Chen",
"Feng Zhu",
"Rui Zhao",
"Yibing Song"
] | 2310.05136 | 18,082 | https://openreview.net/forum?id=hss35aoQ1Y |
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[] | Poster | [] | The Information Bottleneck (IB) principle offers an information-theoretic framework for analyzing the training process of deep neural networks (DNNs). Its essence lies in tracking the dynamics of two mutual information (MI) values: between the hidden layer output and the DNN input/target. According to the hypothesis put forth by Shwartz-Ziv & Tishby (2017), the training process consists of two distinct phases: fitting and compression. The latter phase is believed to account for the good generalization performance exhibited by DNNs. Due to the challenging nature of estimating MI between high-dimensional random vectors, this hypothesis has only been verified for NNs of tiny sizes or specific types, such as quantized NNs. In this paper, we introduce a framework for conducting IB analysis of general NNs. Our approach leverages the stochastic NN method proposed by Goldfeld et al. (2019) and incorporates a compression step to overcome the obstacles associated with high dimensionality. In other words, we estimate the MI between the compressed representations of high-dimensional random vectors. The proposed method is supported by both theoretical and practical justifications. Notably, we demonstrate the accuracy of our estimator through synthetic experiments featuring predefined MI values and comparison with MINE (Belghazi et al., 2018). Finally, we perform IB analysis on a close-to-real-scale convolutional DNN, which reveals new features of the MI dynamics. | [] | [] | Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression | [
"Ivan Butakov",
"Alexander Tolmachev",
"Sofia Malanchuk",
"Anna Neopryatnaya",
"Alexey Frolov",
"Kirill Andreev"
] | 2305.08013 | 18,081 | https://openreview.net/forum?id=huGECz8dPp |
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[] | Poster | [] | Graph pooling compresses graph information into a compact representation. State-of-the-art graph pooling methods follow a hierarchical approach, which reduces the graph size step-by-step. These methods must balance memory efficiency with preserving node information, depending on whether they use node dropping or node clustering. Additionally, fixed pooling ratios or numbers of pooling layers are predefined for all graphs, which prevents personalized pooling structures from being captured for each individual graph. In this work, inspired by bottom-up grammar induction, we propose an efficient graph parsing algorithm to infer the pooling structure, which then drives graph pooling. The resulting Graph Parsing Network (GPN) adaptively learns personalized pooling structure for each individual graph. GPN benefits from the discrete assignments generated by the graph parsing algorithm, allowing good memory efficiency while preserving node information intact. Experimental results on standard benchmarks demonstrate that GPN outperforms state-of-the-art graph pooling methods in graph classification tasks while being able to achieve competitive performance in node classification tasks. We also conduct a graph reconstruction task to show GPN's ability to preserve node information and measure both memory and time efficiency through relevant tests. | [] | [] | Graph Parsing Networks | [
"Yunchong Song",
"Siyuan Huang",
"Xinbing Wang",
"Chenghu Zhou",
"Zhouhan Lin"
] | 2402.14393 | 18,080 | https://openreview.net/forum?id=hv3SklibkL |
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[] | Spotlight Poster | [] | Classical wisdom in machine learning holds that the generalization error can be decomposed into bias and variance, and these two terms exhibit a \emph{trade-off}. However, in this paper, we show that for an ensemble of deep learning based classification models, bias and variance are \emph{aligned} at a sample level, where squared bias is approximately \emph{equal} to variance for correctly classified sample points. We present empirical evidence confirming this phenomenon in a variety of deep learning models and datasets. Moreover, we study this phenomenon from two theoretical perspectives: calibration and neural collapse. We first show theoretically that under the assumption that the models are well calibrated, we can observe the bias-variance alignment. Second, starting from the picture provided by the neural collapse theory, we show an approximate correlation between bias and variance. | [] | [] | On Bias-Variance Alignment in Deep Models | [
"Lin Chen",
"Michal Lukasik",
"Wittawat Jitkrittum",
"Chong You",
"Sanjiv Kumar"
] | 18,078 | https://openreview.net/forum?id=i2Phucne30 |
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