categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.16747 | null | null | http://arxiv.org/pdf/2405.16747v1 | 2024-05-27T01:31:40Z | 2024-05-27T01:31:40Z | Understanding Linear Probing then Fine-tuning Language Models from NTK
Perspective | The two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), consistently outperforms linear probing (LP) and FT alone in terms of accuracy for both in-distribution (ID) and out-of-distribution (OOD) data. This success is largely attributed to the preservation of pre-trained features, achieved through a near-optimal linear head obtained during LP. However, despite the widespread use of large language models, the exploration of complex architectures such as Transformers remains limited. In this paper, we analyze the training dynamics of LP-FT for classification models on the basis of the neural tangent kernel (NTK) theory. Our analysis decomposes the NTK matrix into two components, highlighting the importance of the linear head norm alongside the prediction accuracy at the start of the FT stage. We also observe a significant increase in the linear head norm during LP, stemming from training with the cross-entropy (CE) loss, which effectively minimizes feature changes. Furthermore, we find that this increased norm can adversely affect model calibration, a challenge that can be addressed by temperature scaling. Additionally, we extend our analysis with the NTK to the low-rank adaptation (LoRA) method and validate its effectiveness. Our experiments with a Transformer-based model on natural language processing tasks across multiple benchmarks confirm our theoretical analysis and demonstrate the effectiveness of LP-FT in fine-tuning language models. Code is available at https://github.com/tom4649/lp-ft_ntk. | [
"['Akiyoshi Tomihari' 'Issei Sato']"
] |
null | null | 2405.16748 | null | null | http://arxiv.org/pdf/2405.16748v1 | 2024-05-27T01:35:14Z | 2024-05-27T01:35:14Z | Hypergraph Laplacian Eigenmaps and Face Recognition Problems | Face recognition is a very important topic in data science and biometric security research areas. It has multiple applications in military, finance, and retail, to name a few. In this paper, the novel hypergraph Laplacian Eigenmaps will be proposed and combine with the k nearest-neighbor method and/or with the kernel ridge regression method to solve the face recognition problem. Experimental results illustrate that the accuracy of the combination of the novel hypergraph Laplacian Eigenmaps and one specific classification system is similar to the accuracy of the combination of the old symmetric normalized hypergraph Laplacian Eigenmaps method and one specific classification system. | [
"['Loc Hoang Tran']"
] |
null | null | 2405.16749 | null | null | http://arxiv.org/pdf/2405.16749v1 | 2024-05-27T01:38:30Z | 2024-05-27T01:38:30Z | DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion
Models | Pretrained diffusion models (DMs) have recently been popularly used in solving inverse problems (IPs). The existing methods mostly interleave iterative steps in the reverse diffusion process and iterative steps to bring the iterates closer to satisfying the measurement constraint. However, such interleaving methods struggle to produce final results that look like natural objects of interest (i.e., manifold feasibility) and fit the measurement (i.e., measurement feasibility), especially for nonlinear IPs. Moreover, their capabilities to deal with noisy IPs with unknown types and levels of measurement noise are unknown. In this paper, we advocate viewing the reverse process in DMs as a function and propose a novel plug-in method for solving IPs using pretrained DMs, dubbed DMPlug. DMPlug addresses the issues of manifold feasibility and measurement feasibility in a principled manner, and also shows great potential for being robust to unknown types and levels of noise. Through extensive experiments across various IP tasks, including two linear and three nonlinear IPs, we demonstrate that DMPlug consistently outperforms state-of-the-art methods, often by large margins especially for nonlinear IPs. The code is available at https://github.com/sun-umn/DMPlug. | [
"['Hengkang Wang' 'Xu Zhang' 'Taihui Li' 'Yuxiang Wan' 'Tiancong Chen'\n 'Ju Sun']"
] |
null | null | 2405.16752 | null | null | http://arxiv.org/pdf/2405.16752v1 | 2024-05-27T01:48:07Z | 2024-05-27T01:48:07Z | Model Ensembling for Constrained Optimization | There is a long history in machine learning of model ensembling, beginning with boosting and bagging and continuing to the present day. Much of this history has focused on combining models for classification and regression, but recently there is interest in more complex settings such as ensembling policies in reinforcement learning. Strong connections have also emerged between ensembling and multicalibration techniques. In this work, we further investigate these themes by considering a setting in which we wish to ensemble models for multidimensional output predictions that are in turn used for downstream optimization. More precisely, we imagine we are given a number of models mapping a state space to multidimensional real-valued predictions. These predictions form the coefficients of a linear objective that we would like to optimize under specified constraints. The fundamental question we address is how to improve and combine such models in a way that outperforms the best of them in the downstream optimization problem. We apply multicalibration techniques that lead to two provably efficient and convergent algorithms. The first of these (the white box approach) requires being given models that map states to output predictions, while the second (the emph{black box} approach) requires only policies (mappings from states to solutions to the optimization problem). For both, we provide convergence and utility guarantees. We conclude by investigating the performance and behavior of the two algorithms in a controlled experimental setting. | [
"['Ira Globus-Harris' 'Varun Gupta' 'Michael Kearns' 'Aaron Roth']"
] |
null | null | 2405.16755 | null | null | http://arxiv.org/pdf/2405.16755v2 | 2024-06-27T17:13:32Z | 2024-05-27T01:54:16Z | CHESS: Contextual Harnessing for Efficient SQL Synthesis | Utilizing large language models (LLMs) for transforming natural language questions into SQL queries (text-to-SQL) is a promising yet challenging approach, particularly when applied to real-world databases with complex and extensive schemas. In particular, effectively incorporating data catalogs and database values for SQL generation remains an obstacle, leading to suboptimal solutions. We address this problem by proposing a new pipeline that effectively retrieves relevant data and context, selects an efficient schema, and synthesizes correct and efficient SQL queries. To increase retrieval precision, our pipeline introduces a hierarchical retrieval method leveraging model-generated keywords, locality-sensitive hashing indexing, and vector databases. Additionally, we have developed an adaptive schema pruning technique that adjusts based on the complexity of the problem and the model's context size. Our approach generalizes to both frontier proprietary models like GPT-4 and open-source models such as Llama-3-70B. Through a series of ablation studies, we demonstrate the effectiveness of each component of our pipeline and its impact on the end-to-end performance. Our method achieves new state-of-the-art performance on the cross-domain challenging BIRD dataset. | [
"['Shayan Talaei' 'Mohammadreza Pourreza' 'Yu-Chen Chang'\n 'Azalia Mirhoseini' 'Amin Saberi']"
] |
null | null | 2405.16756 | null | null | http://arxiv.org/pdf/2405.16756v1 | 2024-05-27T01:58:23Z | 2024-05-27T01:58:23Z | Symmetry-Informed Governing Equation Discovery | Despite the advancements in learning governing differential equations from observations of dynamical systems, data-driven methods are often unaware of fundamental physical laws, such as frame invariance. As a result, these algorithms may search an unnecessarily large space and discover equations that are less accurate or overly complex. In this paper, we propose to leverage symmetry in automated equation discovery to compress the equation search space and improve the accuracy and simplicity of the learned equations. Specifically, we derive equivariance constraints from the time-independent symmetries of ODEs. Depending on the types of symmetries, we develop a pipeline for incorporating symmetry constraints into various equation discovery algorithms, including sparse regression and genetic programming. In experiments across a diverse range of dynamical systems, our approach demonstrates better robustness against noise and recovers governing equations with significantly higher probability than baselines without symmetry. | [
"['Jianke Yang' 'Wang Rao' 'Nima Dehmamy' 'Robin Walters' 'Rose Yu']"
] |
null | null | 2405.16759 | null | null | http://arxiv.org/pdf/2405.16759v1 | 2024-05-27T02:12:39Z | 2024-05-27T02:12:39Z | Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models | We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy growing method for stable training of large-scale, high-resolution models. without the needs for cascaded super-resolution components. The key insight stems from careful pre-training of core components, namely, those responsible for text-to-image alignment {it vs.} high-resolution rendering. We first demonstrate the benefits of scaling a {it Shallow UNet}, with no down(up)-sampling enc(dec)oder. Scaling its deep core layers is shown to improve alignment, object structure, and composition. Building on this core model, we propose a greedy algorithm that grows the architecture into high-resolution end-to-end models, while preserving the integrity of the pre-trained representation, stabilizing training, and reducing the need for large high-resolution datasets. This enables a single stage model capable of generating high-resolution images without the need of a super-resolution cascade. Our key results rely on public datasets and show that we are able to train non-cascaded models up to 8B parameters with no further regularization schemes. Vermeer, our full pipeline model trained with internal datasets to produce 1024x1024 images, without cascades, is preferred by 44.0% vs. 21.4% human evaluators over SDXL. | [
"['Cristina N. Vasconcelos' 'Abdullah Rashwan' 'Austin Waters'\n 'Trevor Walker' 'Keyang Xu' 'Jimmy Yan' 'Rui Qian' 'Shixin Luo'\n 'Zarana Parekh' 'Andrew Bunner' 'Hongliang Fei' 'Roopal Garg' 'Mandy Guo'\n 'Ivana Kajic' 'Yeqing Li' 'Henna Nandwani' 'Jordi Pont-Tuset'\n 'Yasumasa Onoe' 'Sarah Rosston' 'Su Wang' 'Wenlei Zhou' 'Kevin Swersky'\n 'David J. Fleet' 'Jason M. Baldridge' 'Oliver Wang']"
] |
null | null | 2405.16761 | null | null | http://arxiv.org/pdf/2405.16761v1 | 2024-05-27T02:20:55Z | 2024-05-27T02:20:55Z | Masked Face Recognition with Generative-to-Discriminative
Representations | Masked face recognition is important for social good but challenged by diverse occlusions that cause insufficient or inaccurate representations. In this work, we propose a unified deep network to learn generative-to-discriminative representations for facilitating masked face recognition. To this end, we split the network into three modules and learn them on synthetic masked faces in a greedy module-wise pretraining manner. First, we leverage a generative encoder pretrained for face inpainting and finetune it to represent masked faces into category-aware descriptors. Attribute to the generative encoder's ability in recovering context information, the resulting descriptors can provide occlusion-robust representations for masked faces, mitigating the effect of diverse masks. Then, we incorporate a multi-layer convolutional network as a discriminative reformer and learn it to convert the category-aware descriptors into identity-aware vectors, where the learning is effectively supervised by distilling relation knowledge from off-the-shelf face recognition model. In this way, the discriminative reformer together with the generative encoder serves as the pretrained backbone, providing general and discriminative representations towards masked faces. Finally, we cascade one fully-connected layer following by one softmax layer into a feature classifier and finetune it to identify the reformed identity-aware vectors. Extensive experiments on synthetic and realistic datasets demonstrate the effectiveness of our approach in recognizing masked faces. | [
"['Shiming Ge' 'Weijia Guo' 'Chenyu Li' 'Junzheng Zhang' 'Yong Li'\n 'Dan Zeng']"
] |
null | null | 2405.16762 | null | null | http://arxiv.org/pdf/2405.16762v1 | 2024-05-27T02:22:43Z | 2024-05-27T02:22:43Z | Addressing Discretization-Induced Bias in Demographic Prediction | Racial and other demographic imputation is necessary for many applications, especially in auditing disparities and outreach targeting in political campaigns. The canonical approach is to construct continuous predictions -- e.g., based on name and geography -- and then to $textit{discretize}$ the predictions by selecting the most likely class (argmax). We study how this practice produces $textit{discretization bias}$. In particular, we show that argmax labeling, as used by a prominent commercial voter file vendor to impute race/ethnicity, results in a substantial under-count of African-American voters, e.g., by 28.2% points in North Carolina. This bias can have substantial implications in downstream tasks that use such labels. We then introduce a $textit{joint optimization}$ approach -- and a tractable $textit{data-driven thresholding}$ heuristic -- that can eliminate this bias, with negligible individual-level accuracy loss. Finally, we theoretically analyze discretization bias, show that calibrated continuous models are insufficient to eliminate it, and that an approach such as ours is necessary. Broadly, we warn researchers and practitioners against discretizing continuous demographic predictions without considering downstream consequences. | [
"['Evan Dong' 'Aaron Schein' 'Yixin Wang' 'Nikhil Garg']"
] |
null | null | 2405.16763 | null | null | http://arxiv.org/pdf/2405.16763v1 | 2024-05-27T02:24:57Z | 2024-05-27T02:24:57Z | Transport of Algebraic Structure to Latent Embeddings | Machine learning often aims to produce latent embeddings of inputs which lie in a larger, abstract mathematical space. For example, in the field of 3D modeling, subsets of Euclidean space can be embedded as vectors using implicit neural representations. Such subsets also have a natural algebraic structure including operations (e.g., union) and corresponding laws (e.g., associativity). How can we learn to "union" two sets using only their latent embeddings while respecting associativity? We propose a general procedure for parameterizing latent space operations that are provably consistent with the laws on the input space. This is achieved by learning a bijection from the latent space to a carefully designed mirrored algebra which is constructed on Euclidean space in accordance with desired laws. We evaluate these structural transport nets for a range of mirrored algebras against baselines that operate directly on the latent space. Our experiments provide strong evidence that respecting the underlying algebraic structure of the input space is key for learning accurate and self-consistent operations. | [
"['Samuel Pfrommer' 'Brendon G. Anderson' 'Somayeh Sojoudi']"
] |
null | null | 2405.16765 | null | null | http://arxiv.org/pdf/2405.16765v1 | 2024-05-27T02:26:37Z | 2024-05-27T02:26:37Z | Study of Robust Direction Finding Based on Joint Sparse Representation | Standard Direction of Arrival (DOA) estimation methods are typically derived based on the Gaussian noise assumption, making them highly sensitive to outliers. Therefore, in the presence of impulsive noise, the performance of these methods may significantly deteriorate. In this paper, we model impulsive noise as Gaussian noise mixed with sparse outliers. By exploiting their statistical differences, we propose a novel DOA estimation method based on sparse signal recovery (SSR). Furthermore, to address the issue of grid mismatch, we utilize an alternating optimization approach that relies on the estimated outlier matrix and the on-grid DOA estimates to obtain the off-grid DOA estimates. Simulation results demonstrate that the proposed method exhibits robustness against large outliers. | [
"['Y. Li' 'W. Xiao' 'L. Zhao' 'Z. Huang' 'Q. Li' 'L. Li' 'R. C. de Lamare']"
] |
null | null | 2405.16766 | null | null | http://arxiv.org/pdf/2405.16766v1 | 2024-05-27T02:27:28Z | 2024-05-27T02:27:28Z | Reframing the Relationship in Out-of-Distribution Detection | The remarkable achievements of Large Language Models (LLMs) have captivated the attention of both academia and industry, transcending their initial role in dialogue generation. The utilization of LLMs as intermediary agents in various tasks has yielded promising results, sparking a wave of innovation in artificial intelligence. Building on these breakthroughs, we introduce a novel approach that integrates the agent paradigm into the Out-of-distribution (OOD) detection task, aiming to enhance its robustness and adaptability. Our proposed method, Concept Matching with Agent (CMA), employs neutral prompts as agents to augment the CLIP-based OOD detection process. These agents function as dynamic observers and communication hubs, interacting with both In-distribution (ID) labels and data inputs to form vector triangle relationships. This triangular framework offers a more nuanced approach than the traditional binary relationship, allowing for better separation and identification of ID and OOD inputs. Our extensive experimental results showcase the superior performance of CMA over both zero-shot and training-required methods in a diverse array of real-world scenarios. | [
"['YuXiao Lee' 'Xiaofeng Cao']"
] |
null | null | 2405.16770 | null | null | http://arxiv.org/pdf/2405.16770v1 | 2024-05-27T02:42:16Z | 2024-05-27T02:42:16Z | Physics informed cell representations for variational formulation of
multiscale problems | With the rapid advancement of graphical processing units, Physics-Informed Neural Networks (PINNs) are emerging as a promising tool for solving partial differential equations (PDEs). However, PINNs are not well suited for solving PDEs with multiscale features, particularly suffering from slow convergence and poor accuracy. To address this limitation of PINNs, this article proposes physics-informed cell representations for resolving multiscale Poisson problems using a model architecture consisting of multilevel multiresolution grids coupled with a multilayer perceptron (MLP). The grid parameters (i.e., the level-dependent feature vectors) and the MLP parameters (i.e., the weights and biases) are determined using gradient-descent based optimization. The variational (weak) form based loss function accelerates computation by allowing the linear interpolation of feature vectors within grid cells. This cell-based MLP model also facilitates the use of a decoupled training scheme for Dirichlet boundary conditions and a parameter-sharing scheme for periodic boundary conditions, delivering superior accuracy compared to conventional PINNs. Furthermore, the numerical examples highlight improved speed and accuracy in solving PDEs with nonlinear or high-frequency boundary conditions and provide insights into hyperparameter selection. In essence, by cell-based MLP model along with the parallel tiny-cuda-nn library, our implementation improves convergence speed and numerical accuracy. | [
"['Yuxiang Gao' 'Soheil Kolouri' 'Ravindra Duddu']"
] |
null | null | 2405.16771 | null | null | http://arxiv.org/pdf/2405.16771v1 | 2024-05-27T02:42:33Z | 2024-05-27T02:42:33Z | ARC: A Generalist Graph Anomaly Detector with In-Context Learning | Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention. However, current GAD methods necessitate training specific to each dataset, resulting in high training costs, substantial data requirements, and limited generalizability when being applied to new datasets and domains. To address these limitations, this paper proposes ARC, a generalist GAD approach that enables a ``one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. Equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset using few-shot normal samples at the inference stage, without the need for retraining or fine-tuning on the target dataset. ARC comprises three components that are well-crafted for capturing universal graph anomaly patterns: 1) smoothness-based feature Alignment module that unifies the features of different datasets into a common and anomaly-sensitive space; 2) ego-neighbor Residual graph encoder that learns abnormality-related node embeddings; and 3) cross-attentive in-Context anomaly scoring module that predicts node abnormality by leveraging few-shot normal samples. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC. | [
"['Yixin Liu' 'Shiyuan Li' 'Yu Zheng' 'Qingfeng Chen' 'Chengqi Zhang'\n 'Shirui Pan']"
] |
null | null | 2405.16772 | null | null | http://arxiv.org/pdf/2405.16772v1 | 2024-05-27T02:45:01Z | 2024-05-27T02:45:01Z | Balancing User Preferences by Social Networks: A Condition-Guided Social
Recommendation Model for Mitigating Popularity Bias | Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplify the popularity bias in recommendation models, resulting in more frequent recommendation of hot items and fewer long-tail items, but also include a substantial amount of redundant information that is essentially meaningless for the model's performance. Existing social recommendation models fail to address the issues of popularity bias and the redundancy of social information, as they directly characterize social influence across the entire social network without making targeted adjustments. In this paper, we propose a Condition-Guided Social Recommendation Model (named CGSoRec) to mitigate the model's popularity bias by denoising the social network and adjusting the weights of user's social preferences. More specifically, CGSoRec first includes a Condition-Guided Social Denoising Model (CSD) to remove redundant social relations in the social network for capturing users' social preferences with items more precisely. Then, CGSoRec calculates users' social preferences based on denoised social network and adjusts the weights in users' social preferences to make them can counteract the popularity bias present in the recommendation model. At last, CGSoRec includes a Condition-Guided Diffusion Recommendation Model (CGD) to introduce the adjusted social preferences as conditions to control the recommendation results for a debiased direction. Comprehensive experiments on three real-world datasets demonstrate the effectiveness of our proposed method. The code is in: https://github.com/hexin5515/CGSoRec. | [
"['Xin He' 'Wenqi Fan' 'Ruobing Wang' 'Yili Wang' 'Ying Wang' 'Shirui Pan'\n 'Xin Wang']"
] |
null | null | 2405.16783 | null | null | http://arxiv.org/pdf/2405.16783v1 | 2024-05-27T03:10:57Z | 2024-05-27T03:10:57Z | TrojFM: Resource-efficient Backdoor Attacks against Very Large
Foundation Models | One key challenge in backdoor attacks against large foundation models is the resource limits. Backdoor attacks usually require retraining the target model, which is impractical for very large foundation models. Existing backdoor attacks are mainly designed for supervised classifiers or small foundation models (e.g., BERT). None of these attacks has successfully compromised a very large foundation model, such as Llama-3-70B, especially with limited computational resources. In this paper, we propose TrojFM, a novel backdoor attack tailored for very large foundation models. Our primary technical contribution is the development of a novel backdoor injection method. This method forces a backdoored model to generate similar hidden representations for poisoned inputs regardless of their actual semantics. Our approach injects such backdoors by fine-tuning only a very small proportion of model parameters. This enables TrojFM to efficiently launch downstream task-agnostic backdoor attacks against very large foundation models under limited computational resources. Moreover, we optimize the fine-tuning process with our customized QLoRA technique, enabling launching our attack via only~textit{one A100 GPU}. Furthermore, we design a new trigger injection method to ensure our attack stealthiness. Through extensive experiments, we first demonstrate that TrojFM can launch effective backdoor attacks against widely used large GPT-style models without jeopardizing their normal functionalities (and outperforming existing attacks on BERT-style models). Furthermore, we show that TrojFM is resilient to SOTA defenses and is insensitive to changes in key hyper-parameters. Finally, we conduct a resource analysis to quantify that our method can significantly save computational and memory costs compared to existing backdoor attacks. | [
"['Yuzhou. Nie' 'Yanting. Wang' 'Jinyuan. Jia' 'Michael J. De Lucia'\n 'Nathaniel D. Bastian' 'Wenbo. Guo' 'Dawn. Song']"
] |
null | null | 2405.16798 | null | null | http://arxiv.org/pdf/2405.16798v2 | 2024-05-29T16:52:43Z | 2024-05-27T03:35:50Z | Exploring Fairness in Educational Data Mining in the Context of the
Right to be Forgotten | In education data mining (EDM) communities, machine learning has achieved remarkable success in discovering patterns and structures to tackle educational challenges. Notably, fairness and algorithmic bias have gained attention in learning analytics of EDM. With the increasing demand for the right to be forgotten, there is a growing need for machine learning models to forget sensitive data and its impact, particularly within the realm of EDM. The paradigm of selective forgetting, also known as machine unlearning, has been extensively studied to address this need by eliminating the influence of specific data from a pre-trained model without complete retraining. However, existing research assumes that interactive data removal operations are conducted in secure and reliable environments, neglecting potential malicious unlearning requests to undermine the fairness of machine learning systems. In this paper, we introduce a novel class of selective forgetting attacks designed to compromise the fairness of learning models while maintaining their predictive accuracy, thereby preventing the model owner from detecting the degradation in model performance. Additionally, we propose an innovative optimization framework for selective forgetting attacks, capable of generating malicious unlearning requests across various attack scenarios. We validate the effectiveness of our proposed selective forgetting attacks on fairness through extensive experiments using diverse EDM datasets. | [
"['Wei Qian' 'Aobo Chen' 'Chenxu Zhao' 'Yangyi Li' 'Mengdi Huai']"
] |
null | null | 2405.16799 | null | null | http://arxiv.org/pdf/2405.16799v1 | 2024-05-27T03:39:34Z | 2024-05-27T03:39:34Z | Dual-State Personalized Knowledge Tracing with Emotional Incorporation | Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the model's ability to accurately capture the personalized knowledge states of students and reasonably predict their performances. To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework. Specifically, we present a novel Dual-State Personalized Knowledge Tracing with Emotional Incorporation model to achieve this goal: Firstly, we incorporate emotional information into the modeling process of knowledge state, resulting in the Knowledge State Boosting Module. Secondly, we design an Emotional State Tracing Module to monitor students' personalized emotional states, and propose an emotion prediction method based on personalized emotional states. Finally, we apply the predicted emotions to enhance students' response prediction. Furthermore, to extend the generalization capability of our model across different datasets, we design a transferred version of DEKT, named Transfer Learning-based Self-loop model (T-DEKT). Extensive experiments show our method achieves the state-of-the-art performance. | [
"['Shanshan Wang' 'Fangzheng Yuan' 'Keyang Wang' 'Xun Yang' 'Xingyi Zhang'\n 'Meng Wang']"
] |
null | null | 2405.16800 | null | null | http://arxiv.org/pdf/2405.16800v1 | 2024-05-27T03:40:16Z | 2024-05-27T03:40:16Z | TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing
Graph and Text Mutual Transformations | Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum of real-world scenarios. Despite the potential for deeper insights, existing TAG representation learning primarily relies on supervised methods, necessitating extensive labeled data and limiting applicability across diverse contexts. This paper introduces a new self-supervised learning framework, Text-And-Graph Multi-View Alignment (TAGA), which overcomes these constraints by integrating TAGs' structural and semantic dimensions. TAGA constructs two complementary views: Text-of-Graph view, which organizes node texts into structured documents based on graph topology, and the Graph-of-Text view, which converts textual nodes and connections into graph data. By aligning representations from both views, TAGA captures joint textual and structural information. In addition, a novel structure-preserving random walk algorithm is proposed for efficient training on large-sized TAGs. Our framework demonstrates strong performance in zero-shot and few-shot scenarios across eight real-world datasets. | [
"['Zheng Zhang' 'Yuntong Hu' 'Bo Pan' 'Chen Ling' 'Liang Zhao']"
] |
null | null | 2405.16802 | null | null | http://arxiv.org/pdf/2405.16802v3 | 2024-05-29T01:47:35Z | 2024-05-27T03:44:24Z | AutoCV: Empowering Reasoning with Automated Process Labeling via
Confidence Variation | In this work, we propose a novel method named textbf{Auto}mated Process Labeling via textbf{C}onfidence textbf{V}ariation (textbf{textsc{AutoCV}}) to enhance the reasoning capabilities of large language models (LLMs) by automatically annotating the reasoning steps. Our approach begins by training a verification model on the correctness of final answers, enabling it to generate automatic process annotations. This verification model assigns a confidence score to each reasoning step, indicating the probability of arriving at the correct final answer from that point onward. We detect relative changes in the verification's confidence scores across reasoning steps to automatically annotate the reasoning process. This alleviates the need for numerous manual annotations or the high computational costs associated with model-induced annotation approaches. We experimentally validate that the confidence variations learned by the verification model trained on the final answer correctness can effectively identify errors in the reasoning steps. Subsequently, we demonstrate that the process annotations generated by textsc{AutoCV} can improve the accuracy of the verification model in selecting the correct answer from multiple outputs generated by LLMs. Notably, we achieve substantial improvements across five datasets in mathematics and commonsense reasoning. The source code of textsc{AutoCV} is available at url{https://github.com/rookie-joe/AUTOCV}. | [
"['Jianqiao Lu' 'Zhiyang Dou' 'Hongru Wang' 'Zeyu Cao' 'Jianbo Dai'\n 'Yingjia Wan' 'Yinya Huang' 'Zhijiang Guo']"
] |
null | null | 2405.16805 | null | null | http://arxiv.org/pdf/2405.16805v1 | 2024-05-27T03:52:53Z | 2024-05-27T03:52:53Z | Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for
High-Dimensional Zeroth-Order Optimization | We study nonconvex zeroth-order optimization (ZOO) in a high-dimensional space $mathbb R^d$ for functions with approximately $s$-sparse gradients. To reduce the dependence on the dimensionality $d$ in the query complexity, high-dimensional ZOO methods seek to leverage gradient sparsity to design gradient estimators. The previous best method needs $Obig(slogfrac dsbig)$ queries per step to achieve $Obig(frac1Tbig)$ rate of convergence w.r.t. the number T of steps. In this paper, we propose *Gradient Compressed Sensing* (GraCe), a query-efficient and accurate estimator for sparse gradients that uses only $Obig(sloglogfrac dsbig)$ queries per step and still achieves $Obig(frac1Tbig)$ rate of convergence. To our best knowledge, we are the first to achieve a *double-logarithmic* dependence on $d$ in the query complexity under weaker assumptions. Our proposed GraCe generalizes the Indyk--Price--Woodruff (IPW) algorithm in compressed sensing from linear measurements to nonlinear functions. Furthermore, since the IPW algorithm is purely theoretical due to its impractically large constant, we improve the IPW algorithm via our *dependent random partition* technique together with our corresponding novel analysis and successfully reduce the constant by a factor of nearly 4300. Our GraCe is not only theoretically query-efficient but also achieves strong empirical performance. We benchmark our GraCe against 12 existing ZOO methods with 10000-dimensional functions and demonstrate that GraCe significantly outperforms existing methods. | [
"['Ruizhong Qiu' 'Hanghang Tong']"
] |
null | null | 2405.16809 | null | null | http://arxiv.org/pdf/2405.16809v1 | 2024-05-27T03:59:13Z | 2024-05-27T03:59:13Z | Trajectory Data Suffices for Statistically Efficient Learning in Offline
RL with Linear $q^π$-Realizability and Concentrability | We consider offline reinforcement learning (RL) in $H$-horizon Markov decision processes (MDPs) under the linear $q^pi$-realizability assumption, where the action-value function of every policy is linear with respect to a given $d$-dimensional feature function. The hope in this setting is that learning a good policy will be possible without requiring a sample size that scales with the number of states in the MDP. Foster et al. [2021] have shown this to be impossible even under $textit{concentrability}$, a data coverage assumption where a coefficient $C_text{conc}$ bounds the extent to which the state-action distribution of any policy can veer off the data distribution. However, the data in this previous work was in the form of a sequence of individual transitions. This leaves open the question of whether the negative result mentioned could be overcome if the data was composed of sequences of full trajectories. In this work we answer this question positively by proving that with trajectory data, a dataset of size $text{poly}(d,H,C_text{conc})/epsilon^2$ is sufficient for deriving an $epsilon$-optimal policy, regardless of the size of the state space. The main tool that makes this result possible is due to Weisz et al. [2023], who demonstrate that linear MDPs can be used to approximate linearly $q^pi$-realizable MDPs. The connection to trajectory data is that the linear MDP approximation relies on "skipping" over certain states. The associated estimation problems are thus easy when working with trajectory data, while they remain nontrivial when working with individual transitions. The question of computational efficiency under our assumptions remains open. | [
"['Volodymyr Tkachuk' 'Gellért Weisz' 'Csaba Szepesvári']"
] |
null | null | 2405.16819 | null | null | http://arxiv.org/pdf/2405.16819v1 | 2024-05-27T04:33:53Z | 2024-05-27T04:33:53Z | Automatic Domain Adaptation by Transformers in In-Context Learning | Selecting or designing an appropriate domain adaptation algorithm for a given problem remains challenging. This paper presents a Transformer model that can provably approximate and opt for domain adaptation methods for a given dataset in the in-context learning framework, where a foundation model performs new tasks without updating its parameters at test time. Specifically, we prove that Transformers can approximate instance-based and feature-based unsupervised domain adaptation algorithms and automatically select an algorithm suited for a given dataset. Numerical results indicate that in-context learning demonstrates an adaptive domain adaptation surpassing existing methods. | [
"['Ryuichiro Hataya' 'Kota Matsui' 'Masaaki Imaizumi']"
] |
null | null | 2405.16820 | null | null | http://arxiv.org/abs/2405.16820v1 | 2024-05-27T04:38:10Z | 2024-05-27T04:38:10Z | Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT
Even in Low-Resource Settings | The rapid proliferation of generative AI has raised questions about the competitiveness of lower-parameter, locally tunable, open-weight models relative to high-parameter, API-guarded, closed-weight models in terms of performance, domain adaptation, cost, and generalization. Centering under-resourced yet risk-intolerant settings in government, research, and healthcare, we see for-profit closed-weight models as incompatible with requirements for transparency, privacy, adaptability, and standards of evidence. Yet the performance penalty in using open-weight models, especially in low-data and low-resource settings, is unclear. We assess the feasibility of using smaller, open-weight models to replace GPT-4-Turbo in zero-shot, few-shot, and fine-tuned regimes, assuming access to only a single, low-cost GPU. We assess value-sensitive issues around bias, privacy, and abstention on three additional tasks relevant to those topics. We find that with relatively low effort, very low absolute monetary cost, and relatively little data for fine-tuning, small open-weight models can achieve competitive performance in domain-adapted tasks without sacrificing generality. We then run experiments considering practical issues in bias, privacy, and hallucination risk, finding that open models offer several benefits over closed models. We intend this work as a case study in understanding the opportunity cost of reproducibility and transparency over for-profit state-of-the-art zero shot performance, finding this cost to be marginal under realistic settings. | [
"['Robert Wolfe' 'Isaac Slaughter' 'Bin Han' 'Bingbing Wen' 'Yiwei Yang'\n 'Lucas Rosenblatt' 'Bernease Herman' 'Eva Brown' 'Zening Qu' 'Nic Weber'\n 'Bill Howe']"
] |
null | null | 2405.16828 | null | null | http://arxiv.org/pdf/2405.16828v1 | 2024-05-27T04:49:41Z | 2024-05-27T04:49:41Z | Kernel-based optimally weighted conformal prediction intervals | Conformal prediction has been a popular distribution-free framework for uncertainty quantification. In this paper, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI). Specifically, KOWCPI adapts the classic Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data and learns optimal data-adaptive weights. Theoretically, we tackle the challenge of establishing a conditional coverage guarantee for non-exchangeable data under strong mixing conditions on the non-conformity scores. We demonstrate the superior performance of KOWCPI on real time-series against state-of-the-art methods, where KOWCPI achieves narrower confidence intervals without losing coverage. | [
"['Jonghyeok Lee' 'Chen Xu' 'Yao Xie']"
] |
null | null | 2405.16830 | null | null | http://arxiv.org/pdf/2405.16830v2 | 2024-05-28T01:20:43Z | 2024-05-27T04:53:09Z | Structured Graph Network for Constrained Robot Crowd Navigation with Low
Fidelity Simulation | We investigate the feasibility of deploying reinforcement learning (RL) policies for constrained crowd navigation using a low-fidelity simulator. We introduce a representation of the dynamic environment, separating human and obstacle representations. Humans are represented through detected states, while obstacles are represented as computed point clouds based on maps and robot localization. This representation enables RL policies trained in a low-fidelity simulator to deploy in real world with a reduced sim2real gap. Additionally, we propose a spatio-temporal graph to model the interactions between agents and obstacles. Based on the graph, we use attention mechanisms to capture the robot-human, human-human, and human-obstacle interactions. Our method significantly improves navigation performance in both simulated and real-world environments. Video demonstrations can be found at https://sites.google.com/view/constrained-crowdnav/home. | [
"['Shuijing Liu' 'Kaiwen Hong' 'Neeloy Chakraborty'\n 'Katherine Driggs-Campbell']"
] |
null | null | 2405.16833 | null | null | http://arxiv.org/pdf/2405.16833v1 | 2024-05-27T05:04:05Z | 2024-05-27T05:04:05Z | Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning
Large Language Models | While large language models (LLMs) such as Llama-2 or GPT-4 have shown impressive zero-shot performance, fine-tuning is still necessary to enhance their performance for customized datasets, domain-specific tasks, or other private needs. However, fine-tuning all parameters of LLMs requires significant hardware resources, which can be impractical for typical users. Therefore, parameter-efficient fine-tuning such as LoRA have emerged, allowing users to fine-tune LLMs without the need for considerable computing resources, with little performance degradation compared to fine-tuning all parameters. Unfortunately, recent studies indicate that fine-tuning can increase the risk to the safety of LLMs, even when data does not contain malicious content. To address this challenge, we propose Safe LoRA, a simple one-liner patch to the original LoRA implementation by introducing the projection of LoRA weights from selected layers to the safety-aligned subspace, effectively reducing the safety risks in LLM fine-tuning while maintaining utility. It is worth noting that Safe LoRA is a training-free and data-free approach, as it only requires the knowledge of the weights from the base and aligned LLMs. Our extensive experiments demonstrate that when fine-tuning on purely malicious data, Safe LoRA retains similar safety performance as the original aligned model. Moreover, when the fine-tuning dataset contains a mixture of both benign and malicious data, Safe LoRA mitigates the negative effect made by malicious data while preserving performance on downstream tasks. | [
"['Chia-Yi Hsu' 'Yu-Lin Tsai' 'Chih-Hsun Lin' 'Pin-Yu Chen' 'Chia-Mu Yu'\n 'Chun-Ying Huang']"
] |
null | null | 2405.16836 | null | null | http://arxiv.org/pdf/2405.16836v1 | 2024-05-27T05:06:24Z | 2024-05-27T05:06:24Z | Enhancing Fast Feed Forward Networks with Load Balancing and a Master
Leaf Node | Fast feedforward networks (FFFs) are a class of neural networks that exploit the observation that different regions of the input space activate distinct subsets of neurons in wide networks. FFFs partition the input space into separate sections using a differentiable binary tree of neurons and during inference descend the binary tree in order to improve computational efficiency. Inspired by Mixture of Experts (MoE) research, we propose the incorporation of load balancing and Master Leaf techniques into the FFF architecture to improve performance and simplify the training process. We reproduce experiments found in literature and present results on FFF models enhanced using these techniques. The proposed architecture and training recipe achieves up to 16.3% and 3% absolute classification accuracy increase in training and test accuracy, respectively, compared to the original FFF architecture. Additionally, we observe a smaller variance in the results compared to those reported in prior research. These findings demonstrate the potential of integrating MoE-inspired techniques into FFFs for developing more accurate and efficient models. | [
"['Andreas Charalampopoulos' 'Nikolas Chatzis'\n 'Foivos Ntoulas-Panagiotopoulos' 'Charilaos Papaioannou'\n 'Alexandros Potamianos']"
] |
null | null | 2405.16837 | null | null | http://arxiv.org/pdf/2405.16837v1 | 2024-05-27T05:10:49Z | 2024-05-27T05:10:49Z | Enhancing Accuracy in Generative Models via Knowledge Transfer | This paper investigates the accuracy of generative models and the impact of knowledge transfer on their generation precision. Specifically, we examine a generative model for a target task, fine-tuned using a pre-trained model from a source task. Building on the "Shared Embedding" concept, which bridges the source and target tasks, we introduce a novel framework for transfer learning under distribution metrics such as the Kullback-Leibler divergence. This framework underscores the importance of leveraging inherent similarities between diverse tasks despite their distinct data distributions. Our theory suggests that the shared structures can augment the generation accuracy for a target task, reliant on the capability of a source model to identify shared structures and effective knowledge transfer from source to target learning. To demonstrate the practical utility of this framework, we explore the theoretical implications for two specific generative models: diffusion and normalizing flows. The results show enhanced performance in both models over their non-transfer counterparts, indicating advancements for diffusion models and providing fresh insights into normalizing flows in transfer and non-transfer settings. These results highlight the significant contribution of knowledge transfer in boosting the generation capabilities of these models. | [
"['Xinyu Tian' 'Xiaotong Shen']"
] |
null | null | 2405.16843 | null | null | http://arxiv.org/pdf/2405.16843v1 | 2024-05-27T05:32:46Z | 2024-05-27T05:32:46Z | Non-stochastic Bandits With Evolving Observations | We introduce a novel online learning framework that unifies and generalizes pre-established models, such as delayed and corrupted feedback, to encompass adversarial environments where action feedback evolves over time. In this setting, the observed loss is arbitrary and may not correlate with the true loss incurred, with each round updating previous observations adversarially. We propose regret minimization algorithms for both the full-information and bandit settings, with regret bounds quantified by the average feedback accuracy relative to the true loss. Our algorithms match the known regret bounds across many special cases, while also introducing previously unknown bounds. | [
"['Yogev Bar-On' 'Yishay Mansour']"
] |
null | null | 2405.16845 | null | null | http://arxiv.org/pdf/2405.16845v1 | 2024-05-27T05:41:06Z | 2024-05-27T05:41:06Z | On Mesa-Optimization in Autoregressively Trained Transformers: Emergence
and Capability | Autoregressively trained transformers have brought a profound revolution to the world, especially with their in-context learning (ICL) ability to address downstream tasks. Recently, several studies suggest that transformers learn a mesa-optimizer during autoregressive (AR) pretraining to implement ICL. Namely, the forward pass of the trained transformer is equivalent to optimizing an inner objective function in-context. However, whether the practical non-convex training dynamics will converge to the ideal mesa-optimizer is still unclear. Towards filling this gap, we investigate the non-convex dynamics of a one-layer linear causal self-attention model autoregressively trained by gradient flow, where the sequences are generated by an AR process $x_{t+1} = W x_t$. First, under a certain condition of data distribution, we prove that an autoregressively trained transformer learns $W$ by implementing one step of gradient descent to minimize an ordinary least squares (OLS) problem in-context. It then applies the learned $widehat{W}$ for next-token prediction, thereby verifying the mesa-optimization hypothesis. Next, under the same data conditions, we explore the capability limitations of the obtained mesa-optimizer. We show that a stronger assumption related to the moments of data is the sufficient and necessary condition that the learned mesa-optimizer recovers the distribution. Besides, we conduct exploratory analyses beyond the first data condition and prove that generally, the trained transformer will not perform vanilla gradient descent for the OLS problem. Finally, our simulation results verify the theoretical results. | [
"['Chenyu Zheng' 'Wei Huang' 'Rongzhen Wang' 'Guoqiang Wu' 'Jun Zhu'\n 'Chongxuan Li']"
] |
null | null | 2405.16850 | null | null | http://arxiv.org/pdf/2405.16850v1 | 2024-05-27T05:52:13Z | 2024-05-27T05:52:13Z | UniCompress: Enhancing Multi-Data Medical Image Compression with
Knowledge Distillation | In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are constrained by a one-to-one fitting approach that results in lengthy encoding times. Our novel method, ``textbf{UniCompress}'', innovatively extends the compression capabilities of INR by being the first to compress multiple medical data blocks using a single INR network. By employing wavelet transforms and quantization, we introduce a codebook containing frequency domain information as a prior input to the INR network. This enhances the representational power of INR and provides distinctive conditioning for different image blocks. Furthermore, our research introduces a new technique for the knowledge distillation of implicit representations, simplifying complex model knowledge into more manageable formats to improve compression ratios. Extensive testing on CT and electron microscopy (EM) datasets has demonstrated that UniCompress outperforms traditional INR methods and commercial compression solutions like HEVC, especially in complex and high compression scenarios. Notably, compared to existing INR techniques, UniCompress achieves a 4$sim$5 times increase in compression speed, marking a significant advancement in the field of medical image compression. Codes will be publicly available. | [
"['Runzhao Yang' 'Yinda Chen' 'Zhihong Zhang' 'Xiaoyu Liu' 'Zongren Li'\n 'Kunlun He' 'Zhiwei Xiong' 'Jinli Suo' 'Qionghai Dai']"
] |
null | null | 2405.16851 | null | null | http://arxiv.org/pdf/2405.16851v1 | 2024-05-27T05:53:30Z | 2024-05-27T05:53:30Z | Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning | Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation, distinguished by their computational capability and energy efficiency due to precise spiking times and sparse spikes with event-driven computation. A significant question is how SNNs can emulate human-like graph-based reasoning of concepts and relations, especially leveraging the temporal domain optimally. This paper reveals that SNNs, when amalgamated with synaptic delay and temporal coding, are proficient in executing (knowledge) graph reasoning. It is elucidated that spiking time can function as an additional dimension to encode relation properties via a neural-generalized path formulation. Empirical results highlight the efficacy of temporal delay in relation processing and showcase exemplary performance in diverse graph reasoning tasks. The spiking model is theoretically estimated to achieve $20times$ energy savings compared to non-spiking counterparts, deepening insights into the capabilities and potential of biologically inspired SNNs for efficient reasoning. The code is available at https://github.com/pkuxmq/GRSNN. | [
"['Mingqing Xiao' 'Yixin Zhu' 'Di He' 'Zhouchen Lin']"
] |
null | null | 2405.16852 | null | null | http://arxiv.org/pdf/2405.16852v1 | 2024-05-27T05:55:22Z | 2024-05-27T05:55:22Z | EM Distillation for One-step Diffusion Models | While diffusion models can learn complex distributions, sampling requires a computationally expensive iterative process. Existing distillation methods enable efficient sampling, but have notable limitations, such as performance degradation with very few sampling steps, reliance on training data access, or mode-seeking optimization that may fail to capture the full distribution. We propose EM Distillation (EMD), a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of perceptual quality. Our approach is derived through the lens of Expectation-Maximization (EM), where the generator parameters are updated using samples from the joint distribution of the diffusion teacher prior and inferred generator latents. We develop a reparametrized sampling scheme and a noise cancellation technique that together stabilizes the distillation process. We further reveal an interesting connection of our method with existing methods that minimize mode-seeking KL. EMD outperforms existing one-step generative methods in terms of FID scores on ImageNet-64 and ImageNet-128, and compares favorably with prior work on distilling text-to-image diffusion models. | [
"['Sirui Xie' 'Zhisheng Xiao' 'Diederik P Kingma' 'Tingbo Hou'\n 'Ying Nian Wu' 'Kevin Patrick Murphy' 'Tim Salimans' 'Ben Poole'\n 'Ruiqi Gao']"
] |
null | null | 2405.16861 | null | null | http://arxiv.org/pdf/2405.16861v1 | 2024-05-27T06:26:55Z | 2024-05-27T06:26:55Z | NCIDiff: Non-covalent Interaction-generative Diffusion Model for
Improving Reliability of 3D Molecule Generation Inside Protein Pocket | Advancements in deep generative modeling have changed the paradigm of drug discovery. Among such approaches, target-aware methods that exploit 3D structures of protein pockets were spotlighted for generating ligand molecules with their plausible binding modes. While docking scores superficially assess the quality of generated ligands, closer inspection of the binding structures reveals the inconsistency in local interactions between a pocket and generated ligands. Here, we address the issue by explicitly generating non-covalent interactions (NCIs), which are universal patterns throughout protein-ligand complexes. Our proposed model, NCIDiff, simultaneously denoises NCI types of protein-ligand edges along with a 3D graph of a ligand molecule during the sampling. With the NCI-generating strategy, our model generates ligands with more reliable NCIs, especially outperforming the baseline diffusion-based models. We further adopted inpainting techniques on NCIs to further improve the quality of the generated molecules. Finally, we showcase the applicability of NCIDiff on drug design tasks for real-world settings with specialized objectives by guiding the generation process with desired NCI patterns. | [
"['Joongwon Lee' 'Wonho Zhung' 'Woo Youn Kim']"
] |
null | null | 2405.16865 | null | null | http://arxiv.org/pdf/2405.16865v1 | 2024-05-27T06:31:39Z | 2024-05-27T06:31:39Z | An Investigation of Conformal Isometry Hypothesis for Grid Cells | This paper investigates the conformal isometry hypothesis as a potential explanation for the emergence of hexagonal periodic patterns in the response maps of grid cells. The hypothesis posits that the activities of the population of grid cells form a high-dimensional vector in the neural space, representing the agent's self-position in 2D physical space. As the agent moves in the 2D physical space, the vector rotates in a 2D manifold in the neural space, driven by a recurrent neural network. The conformal isometry hypothesis proposes that this 2D manifold in the neural space is a conformally isometric embedding of the 2D physical space, in the sense that local displacements of the vector in neural space are proportional to local displacements of the agent in the physical space. Thus the 2D manifold forms an internal map of the 2D physical space, equipped with an internal metric. In this paper, we conduct numerical experiments to show that this hypothesis underlies the hexagon periodic patterns of grid cells. We also conduct theoretical analysis to further support this hypothesis. In addition, we propose a conformal modulation of the input velocity of the agent so that the recurrent neural network of grid cells satisfies the conformal isometry hypothesis automatically. To summarize, our work provides numerical and theoretical evidences for the conformal isometry hypothesis for grid cells and may serve as a foundation for further development of normative models of grid cells and beyond. | [
"['Dehong Xu' 'Ruiqi Gao' 'Wen-Hao Zhang' 'Xue-Xin Wei' 'Ying Nian Wu']"
] |
null | null | 2405.16876 | null | null | http://arxiv.org/pdf/2405.16876v2 | 2024-05-28T03:24:20Z | 2024-05-27T06:48:58Z | Transfer Learning for Diffusion Models | Diffusion models, a specific type of generative model, have achieved unprecedented performance in recent years and consistently produce high-quality synthetic samples. A critical prerequisite for their notable success lies in the presence of a substantial number of training samples, which can be impractical in real-world applications due to high collection costs or associated risks. Consequently, various finetuning and regularization approaches have been proposed to transfer knowledge from existing pre-trained models to specific target domains with limited data. This paper introduces the Transfer Guided Diffusion Process (TGDP), a novel approach distinct from conventional finetuning and regularization methods. We prove that the optimal diffusion model for the target domain integrates pre-trained diffusion models on the source domain with additional guidance from a domain classifier. We further extend TGDP to a conditional version for modeling the joint distribution of data and its corresponding labels, together with two additional regularization terms to enhance the model performance. We validate the effectiveness of TGDP on Gaussian mixture simulations and on real electrocardiogram (ECG) datasets. | [
"['Yidong Ouyang' 'Liyan Xie' 'Hongyuan Zha' 'Guang Cheng']"
] |
null | null | 2405.16877 | null | null | http://arxiv.org/pdf/2405.16877v1 | 2024-05-27T06:49:39Z | 2024-05-27T06:49:39Z | Are Self-Attentions Effective for Time Series Forecasting? | Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformer models have dramatically shifted the landscape of forecasting, their effectiveness remains debated. Recent findings have indicated that simpler linear models might outperform complex Transformer-based approaches, highlighting the potential for more streamlined architectures. In this paper, we shift focus from the overall architecture of the Transformer to the effectiveness of self-attentions for time series forecasting. To this end, we introduce a new architecture, Cross-Attention-only Time Series transformer (CATS), that rethinks the traditional Transformer framework by eliminating self-attention and leveraging cross-attention mechanisms instead. By establishing future horizon-dependent parameters as queries and enhanced parameter sharing, our model not only improves long-term forecasting accuracy but also reduces the number of parameters and memory usage. Extensive experiment across various datasets demonstrates that our model achieves superior performance with the lowest mean squared error and uses fewer parameters compared to existing models. | [
"['Dongbin Kim' 'Jinseong Park' 'Jaewook Lee' 'Hoki Kim']"
] |
null | null | 2405.16879 | null | null | http://arxiv.org/pdf/2405.16879v1 | 2024-05-27T06:50:00Z | 2024-05-27T06:50:00Z | Unsupervised Generative Feature Transformation via Graph Contrastive
Pre-training and Multi-objective Fine-tuning | Feature transformation is to derive a new feature set from original features to augment the AI power of data. In many science domains such as material performance screening, while feature transformation can model material formula interactions and compositions and discover performance drivers, supervised labels are collected from expensive and lengthy experiments. This issue motivates an Unsupervised Feature Transformation Learning (UFTL) problem. Prior literature, such as manual transformation, supervised feedback guided search, and PCA, either relies on domain knowledge or expensive supervised feedback, or suffers from large search space, or overlooks non-linear feature-feature interactions. UFTL imposes a major challenge on existing methods: how to design a new unsupervised paradigm that captures complex feature interactions and avoids large search space? To fill this gap, we connect graph, contrastive, and generative learning to develop a measurement-pretrain-finetune paradigm for UFTL. For unsupervised feature set utility measurement, we propose a feature value consistency preservation perspective and develop a mean discounted cumulative gain like unsupervised metric to evaluate feature set utility. For unsupervised feature set representation pretraining, we regard a feature set as a feature-feature interaction graph, and develop an unsupervised graph contrastive learning encoder to embed feature sets into vectors. For generative transformation finetuning, we regard a feature set as a feature cross sequence and feature transformation as sequential generation. We develop a deep generative feature transformation model that coordinates the pretrained feature set encoder and the gradient information extracted from a feature set utility evaluator to optimize a transformed feature generator. | [
"['Wangyang Ying' 'Dongjie Wang' 'Xuanming Hu' 'Yuanchun Zhou'\n 'Charu C. Aggarwal' 'Yanjie Fu']"
] |
null | null | 2405.16883 | null | null | http://arxiv.org/pdf/2405.16883v2 | 2024-06-20T06:24:23Z | 2024-05-27T06:59:20Z | Scorch: A Library for Sparse Deep Learning | The rapid growth in the size of deep learning models strains the capabilities of traditional dense computation paradigms. Leveraging sparse computation has become increasingly popular for training and deploying large-scale models, but existing deep learning frameworks lack extensive support for sparse operations. To bridge this gap, we introduce Scorch, a library that seamlessly integrates efficient sparse tensor computation into the PyTorch ecosystem, with an initial focus on inference workloads on CPUs. Scorch provides a flexible and intuitive interface for sparse tensors, supporting diverse sparse data structures. Scorch introduces a compiler stack that automates key optimizations, including automatic loop ordering, tiling, and format inference. Combined with a runtime that adapts its execution to both dense and sparse data, Scorch delivers substantial speedups over hand-written PyTorch Sparse (torch.sparse) operations without sacrificing usability. More importantly, Scorch enables efficient computation of complex sparse operations that lack hand-optimized PyTorch implementations. This flexibility is crucial for exploring novel sparse architectures. We demonstrate Scorch's ease of use and performance gains on diverse deep learning models across multiple domains. With only minimal code changes, Scorch achieves 1.05-5.78x speedups over PyTorch Sparse on end-to-end tasks. Scorch's seamless integration and performance gains make it a valuable addition to the PyTorch ecosystem. We believe Scorch will enable wider exploration of sparsity as a tool for scaling deep learning and inform the development of other sparse libraries. | [
"['Bobby Yan' 'Alexander J. Root' 'Trevor Gale' 'David Broman'\n 'Fredrik Kjolstad']"
] |
null | null | 2405.16899 | null | null | http://arxiv.org/pdf/2405.16899v1 | 2024-05-27T07:46:36Z | 2024-05-27T07:46:36Z | Partial Models for Building Adaptive Model-Based Reinforcement Learning
Agents | In neuroscience, one of the key behavioral tests for determining whether a subject of study exhibits model-based behavior is to study its adaptiveness to local changes in the environment. In reinforcement learning, however, recent studies have shown that modern model-based agents display poor adaptivity to such changes. The main reason for this is that modern agents are typically designed to improve sample efficiency in single task settings and thus do not take into account the challenges that can arise in other settings. In local adaptation settings, one particularly important challenge is in quickly building and maintaining a sufficiently accurate model after a local change. This is challenging for deep model-based agents as their models and replay buffers are monolithic structures lacking distribution shift handling capabilities. In this study, we show that the conceptually simple idea of partial models can allow deep model-based agents to overcome this challenge and thus allow for building locally adaptive model-based agents. By modeling the different parts of the state space through different models, the agent can not only maintain a model that is accurate across the state space, but it can also quickly adapt it in the presence of a local change in the environment. We demonstrate this by showing that the use of partial models in agents such as deep Dyna-Q, PlaNet and Dreamer can allow for them to effectively adapt to the local changes in their environments. | [
"['Safa Alver' 'Ali Rahimi-Kalahroudi' 'Doina Precup']"
] |
null | null | 2405.16901 | null | null | http://arxiv.org/pdf/2405.16901v2 | 2024-05-28T11:29:06Z | 2024-05-27T07:49:30Z | Recurrent and Convolutional Neural Networks in Classification of EEG
Signal for Guided Imagery and Mental Workload Detection | The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Possible support for the therapists can be estimation of the time at which subject goes into deep relaxation. This paper presents the results of the investigations of a cohort of 26 students exposed to Guided Imagery relaxation technique and mental task workloads conducted with the use of dense array electroencephalographic amplifier. The research reported herein aimed at verification whether it is possible to detect differences between those two states and to classify them using deep learning methods and recurrent neural networks such as EEGNet, Long Short-Term Memory-based classifier, 1D Convolutional Neural Network and hybrid model of 1D Convolutional Neural Network and Long Short-Term Memory. The data processing pipeline was presented from the data acquisition, through the initial data cleaning, preprocessing and postprocessing. The classification was based on two datasets: one of them using 26 so-called cognitive electrodes and the other one using signal collected from 256 channels. So far there have not been such comparisons in the application being discussed. The classification results are presented by the validation metrics such as: accuracy, recall, precision, F1-score and loss for each case. It turned out that it is not necessary to collect signals from all electrodes as classification of the cognitive ones gives the results similar to those obtained for the full signal and extending input to 256 channels does not add much value. In Disscussion there were proposed an optimal classifier as well as some suggestions concerning the prospective development of the project. | [
"['Filip Postepski' 'Grzegorz M. Wojcik' 'Krzysztof Wrobel'\n 'Andrzej Kawiak' 'Katarzyna Zemla' 'Grzegorz Sedek']"
] |
null | null | 2405.16902 | null | null | http://arxiv.org/pdf/2405.16902v2 | 2024-06-19T09:37:53Z | 2024-05-27T07:50:09Z | Predicting from a Different Perspective: A Re-ranking Model for
Inductive Knowledge Graph Completion | Rule-induction models have demonstrated great power in the inductive setting of knowledge graph completion. In this setting, the models are tested on a knowledge graph entirely composed of unseen entities. These models learn relation patterns as rules by utilizing subgraphs. Providing the same inputs with different rules leads to differences in the model's predictions. In this paper, we focus on the behavior of such models. We propose a re-ranking-based model called ReDistLP (Re-ranking with a Distinct Model for Link Prediction). This model enhances the effectiveness of re-ranking by leveraging the difference in the predictions between the initial retriever and the re-ranker. ReDistLP outperforms the state-of-the-art methods in 2 out of 3 benchmarks. | [
"['Yuki Iwamoto' 'Ken Kaneiwa']"
] |
null | null | 2405.16906 | null | null | http://arxiv.org/pdf/2405.16906v1 | 2024-05-27T07:55:27Z | 2024-05-27T07:55:27Z | Harnessing the Power of Vicinity-Informed Analysis for Classification
under Covariate Shift | Transfer learning enhances prediction accuracy on a target distribution by leveraging data from a source distribution, demonstrating significant benefits in various applications. This paper introduces a novel dissimilarity measure that utilizes vicinity information, i.e., the local structure of data points, to analyze the excess error in classification under covariate shift, a transfer learning setting where marginal feature distributions differ but conditional label distributions remain the same. We characterize the excess error using the proposed measure and demonstrate faster or competitive convergence rates compared to previous techniques. Notably, our approach is effective in situations where the non-absolute continuousness assumption, which often appears in real-world applications, holds. Our theoretical analysis bridges the gap between current theoretical findings and empirical observations in transfer learning, particularly in scenarios with significant differences between source and target distributions. | [
"['Mitsuhiro Fujikawa' 'Yohei Akimoto' 'Jun Sakuma' 'Kazuto Fukuchi']"
] |
null | null | 2405.16907 | null | null | http://arxiv.org/pdf/2405.16907v3 | 2024-06-12T05:32:32Z | 2024-05-27T07:55:45Z | GTA: Generative Trajectory Augmentation with Guidance for Offline
Reinforcement Learning | Offline Reinforcement Learning (Offline RL) presents challenges of learning effective decision-making policies from static datasets without any online interactions. Data augmentation techniques, such as noise injection and data synthesizing, aim to improve Q-function approximation by smoothing the learned state-action region. However, these methods often fall short of directly improving the quality of offline datasets, leading to suboptimal results. In response, we introduce textbf{GTA}, Generative Trajectory Augmentation, a novel generative data augmentation approach designed to enrich offline data by augmenting trajectories to be both high-rewarding and dynamically plausible. GTA applies a diffusion model within the data augmentation framework. GTA partially noises original trajectories and then denoises them with classifier-free guidance via conditioning on amplified return value. Our results show that GTA, as a general data augmentation strategy, enhances the performance of widely used offline RL algorithms in both dense and sparse reward settings. Furthermore, we conduct a quality analysis of data augmented by GTA and demonstrate that GTA improves the quality of the data. Our code is available at https://github.com/Jaewoopudding/GTA | [
"['Jaewoo Lee' 'Sujin Yun' 'Taeyoung Yun' 'Jinkyoo Park']"
] |
null | null | 2405.16915 | null | null | http://arxiv.org/pdf/2405.16915v1 | 2024-05-27T08:08:51Z | 2024-05-27T08:08:51Z | Multilingual Diversity Improves Vision-Language Representations | Massive web-crawled image-text datasets lay the foundation for recent progress in multimodal learning. These datasets are designed with the goal of training a model to do well on standard computer vision benchmarks, many of which, however, have been shown to be English-centric (e.g., ImageNet). Consequently, existing data curation techniques gravitate towards using predominantly English image-text pairs and discard many potentially useful non-English samples. Our work questions this practice. Multilingual data is inherently enriching not only because it provides a gateway to learn about culturally salient concepts, but also because it depicts common concepts differently from monolingual data. We thus conduct a systematic study to explore the performance benefits of using more samples of non-English origins with respect to English vision tasks. By translating all multilingual image-text pairs from a raw web crawl to English and re-filtering them, we increase the prevalence of (translated) multilingual data in the resulting training set. Pre-training on this dataset outperforms using English-only or English-dominated datasets on ImageNet, ImageNet distribution shifts, image-English-text retrieval and on average across 38 tasks from the DataComp benchmark. On a geographically diverse task like GeoDE, we also observe improvements across all regions, with the biggest gain coming from Africa. In addition, we quantitatively show that English and non-English data are significantly different in both image and (translated) text space. We hope that our findings motivate future work to be more intentional about including multicultural and multilingual data, not just when non-English or geographically diverse tasks are involved, but to enhance model capabilities at large. | [
"['Thao Nguyen' 'Matthew Wallingford' 'Sebastin Santy' 'Wei-Chiu Ma'\n 'Sewoong Oh' 'Ludwig Schmidt' 'Pang Wei Koh' 'Ranjay Krishna']"
] |
null | null | 2405.16918 | null | null | http://arxiv.org/pdf/2405.16918v1 | 2024-05-27T08:10:46Z | 2024-05-27T08:10:46Z | The Uncanny Valley: Exploring Adversarial Robustness from a Flatness
Perspective | Flatness of the loss surface not only correlates positively with generalization but is also related to adversarial robustness, since perturbations of inputs relate non-linearly to perturbations of weights. In this paper, we empirically analyze the relation between adversarial examples and relative flatness with respect to the parameters of one layer. We observe a peculiar property of adversarial examples: during an iterative first-order white-box attack, the flatness of the loss surface measured around the adversarial example first becomes sharper until the label is flipped, but if we keep the attack running it runs into a flat uncanny valley where the label remains flipped. We find this phenomenon across various model architectures and datasets. Our results also extend to large language models (LLMs), but due to the discrete nature of the input space and comparatively weak attacks, the adversarial examples rarely reach a truly flat region. Most importantly, this phenomenon shows that flatness alone cannot explain adversarial robustness unless we can also guarantee the behavior of the function around the examples. We theoretically connect relative flatness to adversarial robustness by bounding the third derivative of the loss surface, underlining the need for flatness in combination with a low global Lipschitz constant for a robust model. | [
"['Nils Philipp Walter' 'Linara Adilova' 'Jilles Vreeken' 'Michael Kamp']"
] |
null | null | 2405.16922 | null | null | http://arxiv.org/pdf/2405.16922v1 | 2024-05-27T08:13:39Z | 2024-05-27T08:13:39Z | Theories of synaptic memory consolidation and intelligent plasticity for
continual learning | Humans and animals learn throughout life. Such continual learning is crucial for intelligence. In this chapter, we examine the pivotal role plasticity mechanisms with complex internal synaptic dynamics could play in enabling this ability in neural networks. By surveying theoretical research, we highlight two fundamental enablers for continual learning. First, synaptic plasticity mechanisms must maintain and evolve an internal state over several behaviorally relevant timescales. Second, plasticity algorithms must leverage the internal state to intelligently regulate plasticity at individual synapses to facilitate the seamless integration of new memories while avoiding detrimental interference with existing ones. Our chapter covers successful applications of these principles to deep neural networks and underscores the significance of synaptic metaplasticity in sustaining continual learning capabilities. Finally, we outline avenues for further research to understand the brain's superb continual learning abilities and harness similar mechanisms for artificial intelligence systems. | [
"['Friedemann Zenke' 'Axel Laborieux']"
] |
null | null | 2405.16924 | null | null | http://arxiv.org/pdf/2405.16924v1 | 2024-05-27T08:17:49Z | 2024-05-27T08:17:49Z | Demystifying amortized causal discovery with transformers | Supervised learning approaches for causal discovery from observational data often achieve competitive performance despite seemingly avoiding explicit assumptions that traditional methods make for identifiability. In this work, we investigate CSIvA (Ke et al., 2023), a transformer-based model promising to train on synthetic data and transfer to real data. First, we bridge the gap with existing identifiability theory and show that constraints on the training data distribution implicitly define a prior on the test observations. Consistent with classical approaches, good performance is achieved when we have a good prior on the test data, and the underlying model is identifiable. At the same time, we find new trade-offs. Training on datasets generated from different classes of causal models, unambiguously identifiable in isolation, improves the test generalization. Performance is still guaranteed, as the ambiguous cases resulting from the mixture of identifiable causal models are unlikely to occur (which we formally prove). Overall, our study finds that amortized causal discovery still needs to obey identifiability theory, but it also differs from classical methods in how the assumptions are formulated, trading more reliance on assumptions on the noise type for fewer hypotheses on the mechanisms. | [
"['Francesco Montagna' 'Max Cairney-Leeming' 'Dhanya Sridhar'\n 'Francesco Locatello']"
] |
null | null | 2405.16951 | null | null | http://arxiv.org/pdf/2405.16951v1 | 2024-05-27T08:42:42Z | 2024-05-27T08:42:42Z | Fast ML-driven Analog Circuit Layout using Reinforcement Learning and
Steiner Trees | This paper presents an artificial intelligence driven methodology to reduce the bottleneck often encountered in the analog ICs layout phase. We frame the floorplanning problem as a Markov Decision Process and leverage reinforcement learning for automatic placement generation under established topological constraints. Consequently, we introduce Steiner tree-based methods for the global routing step and generate guiding paths to be used to connect every circuit block. Finally, by integrating these solutions into a procedural generation framework, we present a unified pipeline that bridges the divide between circuit design and verification steps. Experimental results demonstrate the efficacy in generating complete layouts, eventually reducing runtimes to 1.5% compared to manual efforts. | [
"['Davide Basso' 'Luca Bortolussi' 'Mirjana Videnovic-Misic' 'Husni Habal']"
] |
null | null | 2405.16954 | null | null | http://arxiv.org/pdf/2405.16954v2 | 2024-06-23T12:34:06Z | 2024-05-27T08:46:28Z | Convergence of SGD with momentum in the nonconvex case: A time
window-based analysis | We propose a novel time window-based analysis technique to investigate the convergence properties of the stochastic gradient descent method with momentum (SGDM) in nonconvex settings. Despite its popularity, the convergence behavior of SGDM remains less understood in nonconvex scenarios. This is primarily due to the absence of a sufficient descent property and challenges in simultaneously controlling the momentum and stochastic errors in an almost sure sense. To address these challenges, we investigate the behavior of SGDM over specific time windows, rather than examining the descent of consecutive iterates as in traditional studies. This time window-based approach simplifies the convergence analysis and enables us to establish the first iterate convergence result for SGDM under the Kurdyka-Lojasiewicz (KL) property. We further provide local convergence rates which depend on the underlying KL exponent and the utilized step size schemes. | [
"['Junwen Qiu' 'Bohao Ma' 'Andre Milzarek']"
] |
null | null | 2405.16956 | null | null | http://arxiv.org/pdf/2405.16956v2 | 2024-06-03T10:42:50Z | 2024-05-27T08:46:57Z | Functional Programming Paradigm of Python for Scientific Computation
Pipeline Integration | The advent of modern data processing has led to an increasing tendency towards interdisciplinarity, which frequently involves the importation of different technical approaches. Consequently, there is an urgent need for a unified data control system to facilitate the integration of varying libraries. This integration is of profound significance in accelerating prototype verification, optimising algorithm performance and minimising maintenance costs. This paper presents a novel functional programming (FP) paradigm based on the Python architecture and associated suites in programming practice, designed for the integration of pipelines of different data mapping operations. In particular, the solution is intended for the integration of scientific computation flows, which affords a robust yet flexible solution for the aforementioned challenges. | [
"['Chen Zhang' 'Lecheng Jia' 'Wei Zhang' 'Ning Wen']"
] |
null | null | 2405.16958 | null | null | http://arxiv.org/pdf/2405.16958v1 | 2024-05-27T08:53:24Z | 2024-05-27T08:53:24Z | Large Deviations of Gaussian Neural Networks with ReLU activation | We prove a large deviation principle for deep neural networks with Gaussian weights and (at most linearly growing) activation functions. This generalises earlier work, in which bounded and continuous activation functions were considered. In practice, linearly growing activation functions such as ReLU are most commonly used. We furthermore simplify previous expressions for the rate function and a give power-series expansions for the ReLU case. | [
"['Quirin Vogel']"
] |
null | null | 2405.16966 | null | null | http://arxiv.org/pdf/2405.16966v1 | 2024-05-27T09:00:30Z | 2024-05-27T09:00:30Z | Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data | We consider the distributed learning problem with data dispersed across multiple workers under the orchestration of a central server. Asynchronous Stochastic Gradient Descent (SGD) has been widely explored in such a setting to reduce the synchronization overhead associated with parallelization. However, the performance of asynchronous SGD algorithms often depends on a bounded dissimilarity condition among the workers' local data, a condition that can drastically affect their efficiency when the workers' data are highly heterogeneous. To overcome this limitation, we introduce the textit{dual-delayed asynchronous SGD (DuDe-ASGD)} algorithm designed to neutralize the adverse effects of data heterogeneity. DuDe-ASGD makes full use of stale stochastic gradients from all workers during asynchronous training, leading to two distinct time lags in the model parameters and data samples utilized in the server's iterations. Furthermore, by adopting an incremental aggregation strategy, DuDe-ASGD maintains a per-iteration computational cost that is on par with traditional asynchronous SGD algorithms. Our analysis demonstrates that DuDe-ASGD achieves a near-minimax-optimal convergence rate for smooth nonconvex problems, even when the data across workers are extremely heterogeneous. Numerical experiments indicate that DuDe-ASGD compares favorably with existing asynchronous and synchronous SGD-based algorithms. | [
"['Xiaolu Wang' 'Yuchang Sun' 'Hoi-To Wai' 'Jun Zhang']"
] |
null | null | 2405.16971 | null | null | http://arxiv.org/pdf/2405.16971v1 | 2024-05-27T09:08:08Z | 2024-05-27T09:08:08Z | A Correlation- and Mean-Aware Loss Function and Benchmarking Framework
to Improve GAN-based Tabular Data Synthesis | Advancements in science rely on data sharing. In medicine, where personal data are often involved, synthetic tabular data generated by generative adversarial networks (GANs) offer a promising avenue. However, existing GANs struggle to capture the complexities of real-world tabular data, which often contain a mix of continuous and categorical variables with potential imbalances and dependencies. We propose a novel correlation- and mean-aware loss function designed to address these challenges as a regularizer for GANs. To ensure a rigorous evaluation, we establish a comprehensive benchmarking framework using ten real-world datasets and eight established tabular GAN baselines. The proposed loss function demonstrates statistically significant improvements over existing methods in capturing the true data distribution, significantly enhancing the quality of synthetic data generated with GANs. The benchmarking framework shows that the enhanced synthetic data quality leads to improved performance in downstream machine learning (ML) tasks, ultimately paving the way for easier data sharing. | [
"['Minh H. Vu' 'Daniel Edler' 'Carl Wibom' 'Tommy Löfstedt'\n 'Beatrice Melin' 'Martin Rosvall']"
] |
null | null | 2405.16978 | null | null | http://arxiv.org/pdf/2405.16978v1 | 2024-05-27T09:21:40Z | 2024-05-27T09:21:40Z | OSLO: One-Shot Label-Only Membership Inference Attacks | We introduce One-Shot Label-Only (OSLO) membership inference attacks (MIAs), which accurately infer a given sample's membership in a target model's training set with high precision using just emph{a single query}, where the target model only returns the predicted hard label. This is in contrast to state-of-the-art label-only attacks which require $sim6000$ queries, yet get attack precisions lower than OSLO's. OSLO leverages transfer-based black-box adversarial attacks. The core idea is that a member sample exhibits more resistance to adversarial perturbations than a non-member. We compare OSLO against state-of-the-art label-only attacks and demonstrate that, despite requiring only one query, our method significantly outperforms previous attacks in terms of precision and true positive rate (TPR) under the same false positive rates (FPR). For example, compared to previous label-only MIAs, OSLO achieves a TPR that is 7$times$ to 28$times$ stronger under a 0.1% FPR on CIFAR10 for a ResNet model. We evaluated multiple defense mechanisms against OSLO. | [
"['Yuefeng Peng' 'Jaechul Roh' 'Subhransu Maji' 'Amir Houmansadr']"
] |
null | null | 2405.17003 | null | null | http://arxiv.org/pdf/2405.17003v2 | 2024-06-12T13:57:58Z | 2024-05-27T09:47:09Z | Graph Condensation for Open-World Graph Learning | The burgeoning volume of graph data presents significant computational challenges in training graph neural networks (GNNs), critically impeding their efficiency in various applications. To tackle this challenge, graph condensation (GC) has emerged as a promising acceleration solution, focusing on the synthesis of a compact yet representative graph for efficiently training GNNs while retaining performance. Despite the potential to promote scalable use of GNNs, existing GC methods are limited to aligning the condensed graph with merely the observed static graph distribution. This limitation significantly restricts the generalization capacity of condensed graphs, particularly in adapting to dynamic distribution changes. In real-world scenarios, however, graphs are dynamic and constantly evolving, with new nodes and edges being continually integrated. Consequently, due to the limited generalization capacity of condensed graphs, applications that employ GC for efficient GNN training end up with sub-optimal GNNs when confronted with evolving graph structures and distributions in dynamic real-world situations. To overcome this issue, we propose open-world graph condensation (OpenGC), a robust GC framework that integrates structure-aware distribution shift to simulate evolving graph patterns and exploit the temporal environments for invariance condensation. This approach is designed to extract temporal invariant patterns from the original graph, thereby enhancing the generalization capabilities of the condensed graph and, subsequently, the GNNs trained on it. Extensive experiments on both real-world and synthetic evolving graphs demonstrate that OpenGC outperforms state-of-the-art (SOTA) GC methods in adapting to dynamic changes in open-world graph environments. | [
"['Xinyi Gao' 'Tong Chen' 'Wentao Zhang' 'Yayong Li' 'Xiangguo Sun'\n 'Hongzhi Yin']"
] |
null | null | 2405.17017 | null | null | http://arxiv.org/pdf/2405.17017v3 | 2024-06-04T02:58:13Z | 2024-05-27T10:01:52Z | Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean
Field Control Games | Mean Field Control Games (MFCG), introduced in [Angiuli et al., 2022a], represent competitive games between a large number of large collaborative groups of agents in the infinite limit of number and size of groups. In this paper, we prove the convergence of a three-timescale Reinforcement Q-Learning (RL) algorithm to solve MFCG in a model-free approach from the point of view of representative agents. Our analysis uses a Q-table for finite state and action spaces updated at each discrete time-step over an infinite horizon. In [Angiuli et al., 2023], we proved convergence of two-timescale algorithms for MFG and MFC separately highlighting the need to follow multiple population distributions in the MFC case. Here, we integrate this feature for MFCG as well as three rates of update decreasing to zero in the proper ratios. Our technique of proof uses a generalization to three timescales of the two-timescale analysis in [Borkar, 1997]. We give a simple example satisfying the various hypothesis made in the proof of convergence and illustrating the performance of the algorithm. | [
"['Andrea Angiuli' 'Jean-Pierre Fouque' 'Mathieu Laurière' 'Mengrui Zhang']"
] |
null | null | 2405.17027 | null | null | http://arxiv.org/pdf/2405.17027v1 | 2024-05-27T10:30:21Z | 2024-05-27T10:30:21Z | Supervised Batch Normalization | Batch Normalization (BN), a widely-used technique in neural networks, enhances generalization and expedites training by normalizing each mini-batch to the same mean and variance. However, its effectiveness diminishes when confronted with diverse data distributions. To address this challenge, we propose Supervised Batch Normalization (SBN), a pioneering approach. We expand normalization beyond traditional single mean and variance parameters, enabling the identification of data modes prior to training. This ensures effective normalization for samples sharing common features. We define contexts as modes, categorizing data with similar characteristics. These contexts are explicitly defined, such as domains in domain adaptation or modalities in multimodal systems, or implicitly defined through clustering algorithms based on data similarity. We illustrate the superiority of our approach over BN and other commonly employed normalization techniques through various experiments on both single and multi-task datasets. Integrating SBN with Vision Transformer results in a remarkable textit{15.13}% accuracy enhancement on CIFAR-100. Additionally, in domain adaptation scenarios, employing AdaMatch demonstrates an impressive textit{22.25}% accuracy improvement on MNIST and SVHN compared to BN. | [
"['Bilal Faye' 'Mustapha Lebbah' 'Hanane Azzag']"
] |
null | null | 2405.17030 | null | null | http://arxiv.org/pdf/2405.17030v1 | 2024-05-27T10:31:26Z | 2024-05-27T10:31:26Z | SCaRL- A Synthetic Multi-Modal Dataset for Autonomous Driving | We present a novel synthetically generated multi-modal dataset, SCaRL, to enable the training and validation of autonomous driving solutions. Multi-modal datasets are essential to attain the robustness and high accuracy required by autonomous systems in applications such as autonomous driving. As deep learning-based solutions are becoming more prevalent for object detection, classification, and tracking tasks, there is great demand for datasets combining camera, lidar, and radar sensors. Existing real/synthetic datasets for autonomous driving lack synchronized data collection from a complete sensor suite. SCaRL provides synchronized Synthetic data from RGB, semantic/instance, and depth Cameras; Range-Doppler-Azimuth/Elevation maps and raw data from Radar; and 3D point clouds/2D maps of semantic, depth and Doppler data from coherent Lidar. SCaRL is a large dataset based on the CARLA Simulator, which provides data for diverse, dynamic scenarios and traffic conditions. SCaRL is the first dataset to include synthetic synchronized data from coherent Lidar and MIMO radar sensors. The dataset can be accessed here: https://fhr-ihs-sva.pages.fraunhofer.de/asp/scarl/ | [
"['Avinash Nittur Ramesh' 'Aitor Correas-Serrano' 'María González-Huici']"
] |
null | null | 2405.17031 | null | null | http://arxiv.org/pdf/2405.17031v1 | 2024-05-27T10:33:53Z | 2024-05-27T10:33:53Z | Any-step Dynamics Model Improves Future Predictions for Online and
Offline Reinforcement Learning | Model-based methods in reinforcement learning offer a promising approach to enhance data efficiency by facilitating policy exploration within a dynamics model. However, accurately predicting sequential steps in the dynamics model remains a challenge due to the bootstrapping prediction, which attributes the next state to the prediction of the current state. This leads to accumulated errors during model roll-out. In this paper, we propose the Any-step Dynamics Model (ADM) to mitigate the compounding error by reducing bootstrapping prediction to direct prediction. ADM allows for the use of variable-length plans as inputs for predicting future states without frequent bootstrapping. We design two algorithms, ADMPO-ON and ADMPO-OFF, which apply ADM in online and offline model-based frameworks, respectively. In the online setting, ADMPO-ON demonstrates improved sample efficiency compared to previous state-of-the-art methods. In the offline setting, ADMPO-OFF not only demonstrates superior performance compared to recent state-of-the-art offline approaches but also offers better quantification of model uncertainty using only a single ADM. | [
"['Haoxin Lin' 'Yu-Yan Xu' 'Yihao Sun' 'Zhilong Zhang' 'Yi-Chen Li'\n 'Chengxing Jia' 'Junyin Ye' 'Jiaji Zhang' 'Yang Yu']"
] |
null | null | 2405.17034 | null | null | http://arxiv.org/pdf/2405.17034v1 | 2024-05-27T10:40:21Z | 2024-05-27T10:40:21Z | FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks | Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and utility within the framework of spectral graph learning. We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectrum. Our analysis reveals a reduction in the impact of similarity when the eigenvectors associated with the largest magnitude eigenvalue exhibit directional similarity. Based on these theoretical insights, we propose FUGNN, a novel spectral graph learning approach that harmonizes the conflict between fairness and utility. FUGNN ensures algorithmic fairness and utility by truncating the spectrum and optimizing eigenvector distribution during the encoding process. The fairness-aware eigenvector selection reduces the impact of convolution on sensitive features while concurrently minimizing the sacrifice of utility. FUGNN further optimizes the distribution of eigenvectors through a transformer architecture. By incorporating the optimized spectrum into the graph convolution network, FUGNN effectively learns node representations. Experiments on six real-world datasets demonstrate the superiority of FUGNN over baseline methods. The codes are available at https://github.com/yushuowiki/FUGNN. | [
"['Renqiang Luo' 'Huafei Huang' 'Shuo Yu' 'Zhuoyang Han' 'Estrid He'\n 'Xiuzhen Zhang' 'Feng Xia']"
] |
null | null | 2405.17035 | null | null | http://arxiv.org/pdf/2405.17035v2 | 2024-06-27T05:09:57Z | 2024-05-27T10:42:13Z | Glauber Generative Model: Discrete Diffusion Models via Binary
Classification | We introduce the Glauber Generative Model (GGM), a new class of discrete diffusion models, to obtain new samples from a distribution given samples from a discrete space. GGM deploys a discrete Markov chain called the heat bath dynamics (or the Glauber dynamics) to denoise a sequence of noisy tokens to a sample from a joint distribution of discrete tokens. Our novel conceptual framework provides an exact reduction of the task of learning the denoising Markov chain to solving a class of binary classification tasks. More specifically, the model learns to classify a given token in a noisy sequence as signal or noise. In contrast, prior works on discrete diffusion models either solve regression problems to learn importance ratios, or minimize loss functions given by variational approximations. We apply GGM to language modeling and image generation, where images are discretized using image tokenizers like VQGANs. We show that it outperforms existing discrete diffusion models in language generation, and demonstrates strong performance for image generation without using dataset-specific image tokenizers. We also show that our model is capable of performing well in zero-shot control settings like text and image infilling. | [
"['Harshit Varma' 'Dheeraj Nagaraj' 'Karthikeyan Shanmugam']"
] |
null | null | 2405.17039 | null | null | http://arxiv.org/pdf/2405.17039v1 | 2024-05-27T10:45:49Z | 2024-05-27T10:45:49Z | BWArea Model: Learning World Model, Inverse Dynamics, and Policy for
Controllable Language Generation | Large language models (LLMs) have catalyzed a paradigm shift in natural language processing, yet their limited controllability poses a significant challenge for downstream applications. We aim to address this by drawing inspiration from the neural mechanisms of the human brain, specifically Broca's and Wernicke's areas, which are crucial for language generation and comprehension, respectively. In particular, Broca's area receives cognitive decision signals from Wernicke's area, treating the language generation as an intricate decision-making process, which differs from the fully auto-regressive language generation of existing LLMs. In a similar vein, our proposed system, the BWArea model, conceptualizes language generation as a decision-making task. This model has three components: a language world model, an inverse dynamics model, and a cognitive policy. Like Wernicke's area, the inverse dynamics model is designed to deduce the underlying cognitive intentions, or latent actions, behind each token. The BWArea model is amenable to both pre-training and fine-tuning like existing LLMs. With 30B clean pre-training tokens, we have trained a BWArea model, which achieves competitive performance with LLMs of equal size (1B parameters). Unlike fully auto-regressive LLMs, its pre-training performance does not degenerate if dirty data unintentionally appears. This shows the advantage of a decomposed structure of BWArea model in reducing efforts in laborious data selection and labeling. Finally, we reveal that the BWArea model offers enhanced controllability via fine-tuning the cognitive policy with downstream reward metrics, thereby facilitating alignment with greater simplicity. On 9 out of 10 tasks from two suites, TextWorld and BigBench Hard, our method shows superior performance to auto-regressive LLMs. | [
"['Chengxing Jia' 'Pengyuan Wang' 'Ziniu Li' 'Yi-Chen Li' 'Zhilong Zhang'\n 'Nan Tang' 'Yang Yu']"
] |
null | null | 2405.17042 | null | null | http://arxiv.org/pdf/2405.17042v1 | 2024-05-27T10:54:42Z | 2024-05-27T10:54:42Z | LabObf: A Label Protection Scheme for Vertical Federated Learning
Through Label Obfuscation | Split learning, as one of the most common architectures in vertical federated learning, has gained widespread use in industry due to its privacy-preserving characteristics. In this architecture, the party holding the labels seeks cooperation from other parties to improve model performance due to insufficient feature data. Each of these participants has a self-defined bottom model to learn hidden representations from its own feature data and uploads the embedding vectors to the top model held by the label holder for final predictions. This design allows participants to conduct joint training without directly exchanging data. However, existing research points out that malicious participants may still infer label information from the uploaded embeddings, leading to privacy leakage. In this paper, we first propose an embedding extension attack that manually modifies embeddings to undermine existing defense strategies, which rely on constraining the correlation between the embeddings uploaded by participants and the labels. Subsequently, we propose a new label obfuscation defense strategy, called `LabObf', which randomly maps each original one-hot vector label to multiple numerical soft labels with values intertwined, significantly increasing the difficulty for attackers to infer the labels. We conduct experiments on four different types of datasets, and the results show that LabObf can reduce the attacker's success rate to near random guessing while maintaining an acceptable model accuracy. | [
"['Ying He' 'Mingyang Niu' 'Jingyu Hua' 'Yunlong Mao' 'Xu Huang' 'Chen Li'\n 'Sheng Zhong']"
] |
null | null | 2405.17044 | null | null | http://arxiv.org/pdf/2405.17044v1 | 2024-05-27T11:00:51Z | 2024-05-27T11:00:51Z | Generation and human-expert evaluation of interesting research ideas
using knowledge graphs and large language models | Advanced artificial intelligence (AI) systems with access to millions of research papers could inspire new research ideas that may not be conceived by humans alone. However, how interesting are these AI-generated ideas, and how can we improve their quality? Here, we introduce SciMuse, a system that uses an evolving knowledge graph built from more than 58 million scientific papers to generate personalized research ideas via an interface to GPT-4. We conducted a large-scale human evaluation with over 100 research group leaders from the Max Planck Society, who ranked more than 4,000 personalized research ideas based on their level of interest. This evaluation allows us to understand the relationships between scientific interest and the core properties of the knowledge graph. We find that data-efficient machine learning can predict research interest with high precision, allowing us to optimize the interest-level of generated research ideas. This work represents a step towards an artificial scientific muse that could catalyze unforeseen collaborations and suggest interesting avenues for scientists. | [
"['Xuemei Gu' 'Mario Krenn']"
] |
null | null | 2405.17047 | null | null | http://arxiv.org/pdf/2405.17047v1 | 2024-05-27T11:02:21Z | 2024-05-27T11:02:21Z | Interpretable Robotic Manipulation from Language | Humans naturally employ linguistic instructions to convey knowledge, a process that proves significantly more complex for machines, especially within the context of multitask robotic manipulation environments. Natural language, moreover, serves as the primary medium through which humans acquire new knowledge, presenting a potentially intuitive bridge for translating concepts understandable by humans into formats that can be learned by machines. In pursuit of facilitating this integration, we introduce an explainable behavior cloning agent, named Ex-PERACT, specifically designed for manipulation tasks. This agent is distinguished by its hierarchical structure, which incorporates natural language to enhance the learning process. At the top level, the model is tasked with learning a discrete skill code, while at the bottom level, the policy network translates the problem into a voxelized grid and maps the discretized actions to voxel grids. We evaluate our method across eight challenging manipulation tasks utilizing the RLBench benchmark, demonstrating that Ex-PERACT not only achieves competitive policy performance but also effectively bridges the gap between human instructions and machine execution in complex environments. | [
"['Boyuan Zheng' 'Jianlong Zhou' 'Fang Chen']"
] |
null | null | 2405.17049 | null | null | http://arxiv.org/pdf/2405.17049v1 | 2024-05-27T11:03:48Z | 2024-05-27T11:03:48Z | Verifying Properties of Binary Neural Networks Using Sparse Polynomial
Optimization | This paper explores methods for verifying the properties of Binary Neural Networks (BNNs), focusing on robustness against adversarial attacks. Despite their lower computational and memory needs, BNNs, like their full-precision counterparts, are also sensitive to input perturbations. Established methods for solving this problem are predominantly based on Satisfiability Modulo Theories and Mixed-Integer Linear Programming techniques, which are characterized by NP complexity and often face scalability issues. We introduce an alternative approach using Semidefinite Programming relaxations derived from sparse Polynomial Optimization. Our approach, compatible with continuous input space, not only mitigates numerical issues associated with floating-point calculations but also enhances verification scalability through the strategic use of tighter first-order semidefinite relaxations. We demonstrate the effectiveness of our method in verifying robustness against both $|.|_infty$ and $|.|_2$-based adversarial attacks. | [
"['Jianting Yang' 'Srećko Ðurašinović' 'Jean-Bernard Lasserre'\n 'Victor Magron' 'Jun Zhao']"
] |
null | null | 2405.17050 | null | null | http://arxiv.org/pdf/2405.17050v1 | 2024-05-27T11:04:05Z | 2024-05-27T11:04:05Z | HeNCler: Node Clustering in Heterophilous Graphs through Learned
Asymmetric Similarity | Clustering nodes in heterophilous graphs presents unique challenges due to the asymmetric relationships often overlooked by traditional methods, which moreover assume that good clustering corresponds to high intra-cluster and low inter-cluster connectivity. To address these issues, we introduce HeNCler - a novel approach for Heterophilous Node Clustering. Our method begins by defining a weighted kernel singular value decomposition to create an asymmetric similarity graph, applicable to both directed and undirected graphs. We further establish that the dual problem of this formulation aligns with asymmetric kernel spectral clustering, interpreting learned graph similarities without relying on homophily. We demonstrate the ability to solve the primal problem directly, circumventing the computational difficulties of the dual approach. Experimental evidence confirms that HeNCler significantly enhances performance in node clustering tasks within heterophilous graph contexts. | [
"['Sonny Achten' 'Francesco Tonin' 'Volkan Cevher' 'Johan A. K. Suykens']"
] |
null | null | 2405.17051 | null | null | http://arxiv.org/pdf/2405.17051v1 | 2024-05-27T11:07:47Z | 2024-05-27T11:07:47Z | BeamVQ: Aligning Space-Time Forecasting Model via Self-training on
Physics-aware Metrics | Data-driven deep learning has emerged as the new paradigm to model complex physical space-time systems. These data-driven methods learn patterns by optimizing statistical metrics and tend to overlook the adherence to physical laws, unlike traditional model-driven numerical methods. Thus, they often generate predictions that are not physically realistic. On the other hand, by sampling a large amount of high quality predictions from a data-driven model, some predictions will be more physically plausible than the others and closer to what will happen in the future. Based on this observation, we propose emph{Beam search by Vector Quantization} (BeamVQ) to enhance the physical alignment of data-driven space-time forecasting models. The key of BeamVQ is to train model on self-generated samples filtered with physics-aware metrics. To be flexibly support different backbone architectures, BeamVQ leverages a code bank to transform any encoder-decoder model to the continuous state space into discrete codes. Afterwards, it iteratively employs beam search to sample high-quality sequences, retains those with the highest physics-aware scores, and trains model on the new dataset. Comprehensive experiments show that BeamVQ not only gave an average statistical skill score boost for more than 32% for ten backbones on five datasets, but also significantly enhances physics-aware metrics. | [
"['Hao Wu' 'Xingjian Shi' 'Ziyue Huang' 'Penghao Zhao' 'Wei Xiong'\n 'Jinbao Xue' 'Yangyu Tao' 'Xiaomeng Huang' 'Weiyan Wang']"
] |
null | null | 2405.17053 | null | null | http://arxiv.org/pdf/2405.17053v2 | 2024-06-15T07:01:54Z | 2024-05-27T11:18:25Z | WirelessLLM: Empowering Large Language Models Towards Wireless
Intelligence | The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configured, and managed. Recent advancements in Large Language Models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. However, existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks. We first identify three foundational principles that underpin WirelessLLM: knowledge alignment, knowledge fusion, and knowledge evolution. Then, we investigate the enabling technologies to build WirelessLLM, including prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Moreover, we present three case studies to demonstrate the practical applicability and benefits of WirelessLLM for solving typical problems in wireless networks. Finally, we conclude this paper by highlighting key challenges and outlining potential avenues for future research. | [
"['Jiawei Shao' 'Jingwen Tong' 'Qiong Wu' 'Wei Guo' 'Zijian Li'\n 'Zehong Lin' 'Jun Zhang']"
] |
null | null | 2405.17054 | null | null | http://arxiv.org/pdf/2405.17054v1 | 2024-05-27T11:21:26Z | 2024-05-27T11:21:26Z | Improving Data-aware and Parameter-aware Robustness for Continual
Learning | The goal of Continual Learning (CL) task is to continuously learn multiple new tasks sequentially while achieving a balance between the plasticity and stability of new and old knowledge. This paper analyzes that this insufficiency arises from the ineffective handling of outliers, leading to abnormal gradients and unexpected model updates. To address this issue, we enhance the data-aware and parameter-aware robustness of CL, proposing a Robust Continual Learning (RCL) method. From the data perspective, we develop a contrastive loss based on the concepts of uniformity and alignment, forming a feature distribution that is more applicable to outliers. From the parameter perspective, we present a forward strategy for worst-case perturbation and apply robust gradient projection to the parameters. The experimental results on three benchmarks show that the proposed method effectively maintains robustness and achieves new state-of-the-art (SOTA) results. The code is available at: https://github.com/HanxiXiao/RCL | [
"['Hanxi Xiao' 'Fan Lyu']"
] |
null | null | 2405.17059 | null | null | http://arxiv.org/pdf/2405.17059v1 | 2024-05-27T11:29:54Z | 2024-05-27T11:29:54Z | Comparative Study of Machine Learning Algorithms in Detecting
Cardiovascular Diseases | The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics, aiming to enhance early detection, accuracy, and efficiency. This study explores a comparative analysis of various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. By utilising a structured workflow encompassing data collection, preprocessing, model selection and hyperparameter tuning, training, evaluation, and choice of the optimal model, this research addresses the critical need for improved diagnostic tools. The findings highlight the efficacy of ensemble methods and advanced algorithms in providing reliable predictions, thereby offering a comprehensive framework for CVD detection that can be readily implemented and adapted in clinical settings. | [
"['Dayana K' 'S. Nandini' 'Sanjjushri Varshini R']"
] |
null | null | 2405.17060 | null | null | http://arxiv.org/pdf/2405.17060v1 | 2024-05-27T11:31:08Z | 2024-05-27T11:31:08Z | Graph Neural Networks on Quantum Computers | Graph Neural Networks (GNNs) are powerful machine learning models that excel at analyzing structured data represented as graphs, demonstrating remarkable performance in applications like social network analysis and recommendation systems. However, classical GNNs face scalability challenges when dealing with large-scale graphs. This paper proposes frameworks for implementing GNNs on quantum computers to potentially address the challenges. We devise quantum algorithms corresponding to the three fundamental types of classical GNNs: Graph Convolutional Networks, Graph Attention Networks, and Message-Passing GNNs. A complexity analysis of our quantum implementation of the Simplified Graph Convolutional (SGC) Network shows potential quantum advantages over its classical counterpart, with significant improvements in time and space complexities. Our complexities can have trade-offs between the two: when optimizing for minimal circuit depth, our quantum SGC achieves logarithmic time complexity in the input sizes (albeit at the cost of linear space complexity). When optimizing for minimal qubit usage, the quantum SGC exhibits space complexity logarithmic in the input sizes, offering an exponential reduction compared to classical SGCs, while still maintaining better time complexity. These results suggest our Quantum GNN frameworks could efficiently process large-scale graphs. This work paves the way for implementing more advanced Graph Neural Network models on quantum computers, opening new possibilities in quantum machine learning for analyzing graph-structured data. | [
"['Yidong Liao' 'Xiao-Ming Zhang' 'Chris Ferrie']"
] |
null | null | 2405.17061 | null | null | http://arxiv.org/pdf/2405.17061v1 | 2024-05-27T11:31:54Z | 2024-05-27T11:31:54Z | Provably Efficient Reinforcement Learning with Multinomial Logit
Function Approximation | We study a new class of MDPs that employs multinomial logit (MNL) function approximation to ensure valid probability distributions over the state space. Despite its benefits, introducing non-linear function approximation raises significant challenges in both computational and statistical efficiency. The best-known method of Hwang and Oh [2023] has achieved an $widetilde{mathcal{O}}(kappa^{-1}dH^2sqrt{K})$ regret, where $kappa$ is a problem-dependent quantity, $d$ is the feature space dimension, $H$ is the episode length, and $K$ is the number of episodes. While this result attains the same rate in $K$ as the linear cases, the method requires storing all historical data and suffers from an $mathcal{O}(K)$ computation cost per episode. Moreover, the quantity $kappa$ can be exponentially small, leading to a significant gap for the regret compared to the linear cases. In this work, we first address the computational concerns by proposing an online algorithm that achieves the same regret with only $mathcal{O}(1)$ computation cost. Then, we design two algorithms that leverage local information to enhance statistical efficiency. They not only maintain an $mathcal{O}(1)$ computation cost per episode but achieve improved regrets of $widetilde{mathcal{O}}(kappa^{-1/2}dH^2sqrt{K})$ and $widetilde{mathcal{O}}(dH^2sqrt{K} + kappa^{-1}d^2H^2)$ respectively. Finally, we establish a lower bound, justifying the optimality of our results in $d$ and $K$. To the best of our knowledge, this is the first work that achieves almost the same computational and statistical efficiency as linear function approximation while employing non-linear function approximation for reinforcement learning. | [
"['Long-Fei Li' 'Yu-Jie Zhang' 'Peng Zhao' 'Zhi-Hua Zhou']"
] |
null | null | 2405.17066 | null | null | http://arxiv.org/pdf/2405.17066v1 | 2024-05-27T11:37:36Z | 2024-05-27T11:37:36Z | Saturn: Sample-efficient Generative Molecular Design using Memory
Manipulation | Generative molecular design for drug discovery has very recently achieved a wave of experimental validation, with language-based backbones being the most common architectures employed. The most important factor for downstream success is whether an in silico oracle is well correlated with the desired end-point. To this end, current methods use cheaper proxy oracles with higher throughput before evaluating the most promising subset with high-fidelity oracles. The ability to directly optimize high-fidelity oracles would greatly enhance generative design and be expected to improve hit rates. However, current models are not efficient enough to consider such a prospect, exemplifying the sample efficiency problem. In this work, we introduce Saturn, which leverages the Augmented Memory algorithm and demonstrates the first application of the Mamba architecture for generative molecular design. We elucidate how experience replay with data augmentation improves sample efficiency and how Mamba synergistically exploits this mechanism. Saturn outperforms 22 models on multi-parameter optimization tasks relevant to drug discovery and may possess sufficient sample efficiency to consider the prospect of directly optimizing high-fidelity oracles. | [
"['Jeff Guo' 'Philippe Schwaller']"
] |
null | null | 2405.17068 | null | null | http://arxiv.org/pdf/2405.17068v1 | 2024-05-27T11:40:42Z | 2024-05-27T11:40:42Z | The Poisson Midpoint Method for Langevin Dynamics: Provably Efficient
Discretization for Diffusion Models | Langevin Dynamics is a Stochastic Differential Equation (SDE) central to sampling and generative modeling and is implemented via time discretization. Langevin Monte Carlo (LMC), based on the Euler-Maruyama discretization, is the simplest and most studied algorithm. LMC can suffer from slow convergence - requiring a large number of steps of small step-size to obtain good quality samples. This becomes stark in the case of diffusion models where a large number of steps gives the best samples, but the quality degrades rapidly with smaller number of steps. Randomized Midpoint Method has been recently proposed as a better discretization of Langevin dynamics for sampling from strongly log-concave distributions. However, important applications such as diffusion models involve non-log concave densities and contain time varying drift. We propose its variant, the Poisson Midpoint Method, which approximates a small step-size LMC with large step-sizes. We prove that this can obtain a quadratic speed up of LMC under very weak assumptions. We apply our method to diffusion models for image generation and show that it maintains the quality of DDPM with 1000 neural network calls with just 50-80 neural network calls and outperforms ODE based methods with similar compute. | [
"['Saravanan Kandasamy' 'Dheeraj Nagaraj']"
] |
null | null | 2405.17069 | null | null | http://arxiv.org/pdf/2405.17069v1 | 2024-05-27T11:40:50Z | 2024-05-27T11:40:50Z | Training-free Editioning of Text-to-Image Models | Inspired by the software industry's practice of offering different editions or versions of a product tailored to specific user groups or use cases, we propose a novel task, namely, training-free editioning, for text-to-image models. Specifically, we aim to create variations of a base text-to-image model without retraining, enabling the model to cater to the diverse needs of different user groups or to offer distinct features and functionalities. To achieve this, we propose that different editions of a given text-to-image model can be formulated as concept subspaces in the latent space of its text encoder (e.g., CLIP). In such a concept subspace, all points satisfy a specific user need (e.g., generating images of a cat lying on the grass/ground/falling leaves). Technically, we apply Principal Component Analysis (PCA) to obtain the desired concept subspaces from representative text embedding that correspond to a specific user need or requirement. Projecting the text embedding of a given prompt into these low-dimensional subspaces enables efficient model editioning without retraining. Intuitively, our proposed editioning paradigm enables a service provider to customize the base model into its "cat edition" (or other editions) that restricts image generation to cats, regardless of the user's prompt (e.g., dogs, people, etc.). This introduces a new dimension for product differentiation, targeted functionality, and pricing strategies, unlocking novel business models for text-to-image generators. Extensive experimental results demonstrate the validity of our approach and its potential to enable a wide range of customized text-to-image model editions across various domains and applications. | [
"['Jinqi Wang' 'Yunfei Fu' 'Zhangcan Ding' 'Bailin Deng' 'Yu-Kun Lai'\n 'Yipeng Qin']"
] |
null | null | 2405.17070 | null | null | http://arxiv.org/pdf/2405.17070v1 | 2024-05-27T11:41:41Z | 2024-05-27T11:41:41Z | Efficient mid-term forecasting of hourly electricity load using
generalized additive models | Accurate mid-term (weeks to one year) hourly electricity load forecasts are essential for strategic decision-making in power plant operation, ensuring supply security and grid stability, and energy trading. While numerous models effectively predict short-term (hours to a few days) hourly load, mid-term forecasting solutions remain scarce. In mid-term load forecasting, besides daily, weekly, and annual seasonal and autoregressive effects, capturing weather and holiday effects, as well as socio-economic non-stationarities in the data, poses significant modeling challenges. To address these challenges, we propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines and enhanced with autoregressive post-processing. This model uses smoothed temperatures, Error-Trend-Seasonal (ETS) modeled non-stationary states, a nuanced representation of holiday effects with weekday variations, and seasonal information as input. The proposed model is evaluated on load data from 24 European countries. This analysis demonstrates that the model not only has significantly enhanced forecasting accuracy compared to state-of-the-art methods but also offers valuable insights into the influence of individual components on predicted load, given its full interpretability. Achieving performance akin to day-ahead TSO forecasts in fast computation times of a few seconds for several years of hourly data underscores the model's potential for practical application in the power system industry. | [
"['Monika Zimmermann' 'Florian Ziel']"
] |
null | null | 2405.17075 | null | null | http://arxiv.org/pdf/2405.17075v1 | 2024-05-27T11:46:14Z | 2024-05-27T11:46:14Z | Interaction-Force Transport Gradient Flows | This paper presents a new type of gradient flow geometries over non-negative and probability measures motivated via a principled construction that combines the optimal transport and interaction forces modeled by reproducing kernels. Concretely, we propose the interaction-force transport (IFT) gradient flows and its spherical variant via an infimal convolution of the Wasserstein and spherical MMD Riemannian metric tensors. We then develop a particle-based optimization algorithm based on the JKO-splitting scheme of the mass-preserving spherical IFT gradient flows. Finally, we provide both theoretical global exponential convergence guarantees and empirical simulation results for applying the IFT gradient flows to the sampling task of MMD-minimization studied by Arbel et al. [2019]. Furthermore, we prove that the spherical IFT gradient flow enjoys the best of both worlds by providing the global exponential convergence guarantee for both the MMD and KL energy. | [
"['Egor Gladin' 'Pavel Dvurechensky' 'Alexander Mielke' 'Jia-Jie Zhu']"
] |
null | null | 2405.17079 | null | null | http://arxiv.org/pdf/2405.17079v1 | 2024-05-27T11:52:24Z | 2024-05-27T11:52:24Z | Learning with User-Level Local Differential Privacy | User-level privacy is important in distributed systems. Previous research primarily focuses on the central model, while the local models have received much less attention. Under the central model, user-level DP is strictly stronger than the item-level one. However, under the local model, the relationship between user-level and item-level LDP becomes more complex, thus the analysis is crucially different. In this paper, we first analyze the mean estimation problem and then apply it to stochastic optimization, classification, and regression. In particular, we propose adaptive strategies to achieve optimal performance at all privacy levels. Moreover, we also obtain information-theoretic lower bounds, which show that the proposed methods are minimax optimal up to logarithmic factors. Unlike the central DP model, where user-level DP always leads to slower convergence, our result shows that under the local model, the convergence rates are nearly the same between user-level and item-level cases for distributions with bounded support. For heavy-tailed distributions, the user-level rate is even faster than the item-level one. | [
"['Puning Zhao' 'Li Shen' 'Rongfei Fan' 'Qingming Li' 'Huiwen Wu'\n 'Jiafei Wu' 'Zhe Liu']"
] |
null | null | 2405.17081 | null | null | http://arxiv.org/pdf/2405.17081v1 | 2024-05-27T11:54:51Z | 2024-05-27T11:54:51Z | Effective Layer Pruning Through Similarity Metric Perspective | Deep neural networks have been the predominant paradigm in machine learning for solving cognitive tasks. Such models, however, are restricted by a high computational overhead, limiting their applicability and hindering advancements in the field. Extensive research demonstrated that pruning structures from these models is a straightforward approach to reducing network complexity. In this direction, most efforts focus on removing weights or filters. Studies have also been devoted to layer pruning as it promotes superior computational gains. However, layer pruning often hurts the network predictive ability (i.e., accuracy) at high compression rates. This work introduces an effective layer-pruning strategy that meets all underlying properties pursued by pruning methods. Our method estimates the relative importance of a layer using the Centered Kernel Alignment (CKA) metric, employed to measure the similarity between the representations of the unpruned model and a candidate layer for pruning. We confirm the effectiveness of our method on standard architectures and benchmarks, in which it outperforms existing layer-pruning strategies and other state-of-the-art pruning techniques. Particularly, we remove more than 75% of computation while improving predictive ability. At higher compression regimes, our method exhibits negligible accuracy drop, while other methods notably deteriorate model accuracy. Apart from these benefits, our pruned models exhibit robustness to adversarial and out-of-distribution samples. | [
"['Ian Pons' 'Bruno Yamamoto' 'Anna H. Reali Costa' 'Artur Jordao']"
] |
null | null | 2405.17088 | null | null | http://arxiv.org/pdf/2405.17088v1 | 2024-05-27T12:04:36Z | 2024-05-27T12:04:36Z | Phase Transitions in the Output Distribution of Large Language Models | In a physical system, changing parameters such as temperature can induce a phase transition: an abrupt change from one state of matter to another. Analogous phenomena have recently been observed in large language models. Typically, the task of identifying phase transitions requires human analysis and some prior understanding of the system to narrow down which low-dimensional properties to monitor and analyze. Statistical methods for the automated detection of phase transitions from data have recently been proposed within the physics community. These methods are largely system agnostic and, as shown here, can be adapted to study the behavior of large language models. In particular, we quantify distributional changes in the generated output via statistical distances, which can be efficiently estimated with access to the probability distribution over next-tokens. This versatile approach is capable of discovering new phases of behavior and unexplored transitions -- an ability that is particularly exciting in light of the rapid development of language models and their emergent capabilities. | [
"['Julian Arnold' 'Flemming Holtorf' 'Frank Schäfer' 'Niels Lörch']"
] |
null | null | 2405.17094 | null | null | http://arxiv.org/pdf/2405.17094v1 | 2024-05-27T12:10:07Z | 2024-05-27T12:10:07Z | Dual feature reduction for the sparse-group lasso and its adaptive
variant | The sparse-group lasso performs both variable and group selection, making simultaneous use of the strengths of the lasso and group lasso. It has found widespread use in genetics, a field that regularly involves the analysis of high-dimensional data, due to its sparse-group penalty, which allows it to utilize grouping information. However, the sparse-group lasso can be computationally more expensive than both the lasso and group lasso, due to the added shrinkage complexity, and its additional hyper-parameter that needs tuning. In this paper a novel dual feature reduction method, Dual Feature Reduction (DFR), is presented that uses strong screening rules for the sparse-group lasso and the adaptive sparse-group lasso to reduce their input space before optimization. DFR applies two layers of screening and is based on the dual norms of the sparse-group lasso and adaptive sparse-group lasso. Through synthetic and real numerical studies, it is shown that the proposed feature reduction approach is able to drastically reduce the computational cost in many different scenarios. | [
"['Fabio Feser' 'Marina Evangelou']"
] |
null | null | 2405.17097 | null | null | http://arxiv.org/pdf/2405.17097v1 | 2024-05-27T12:12:26Z | 2024-05-27T12:12:26Z | Evaluation of Multi-task Uncertainties in Joint Semantic Segmentation
and Monocular Depth Estimation | While a number of promising uncertainty quantification methods have been proposed to address the prevailing shortcomings of deep neural networks like overconfidence and lack of explainability, quantifying predictive uncertainties in the context of joint semantic segmentation and monocular depth estimation has not been explored yet. Since many real-world applications are multi-modal in nature and, hence, have the potential to benefit from multi-task learning, this is a substantial gap in current literature. To this end, we conduct a comprehensive series of experiments to study how multi-task learning influences the quality of uncertainty estimates in comparison to solving both tasks separately. | [
"['Steven Landgraf' 'Markus Hillemann' 'Theodor Kapler' 'Markus Ulrich']"
] |
null | null | 2405.17098 | null | null | http://arxiv.org/pdf/2405.17098v1 | 2024-05-27T12:12:39Z | 2024-05-27T12:12:39Z | Q-value Regularized Transformer for Offline Reinforcement Learning | Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action distribution based on history trajectory and target returns for each state. However, these methods often struggle with stitching together optimal trajectories from sub-optimal ones due to the inconsistency between the sampled returns within individual trajectories and the optimal returns across multiple trajectories. Fortunately, Dynamic Programming (DP) methods offer a solution by leveraging a value function to approximate optimal future returns for each state, while these techniques are prone to unstable learning behaviors, particularly in long-horizon and sparse-reward scenarios. Building upon these insights, we propose the Q-value regularized Transformer (QT), which combines the trajectory modeling ability of the Transformer with the predictability of optimal future returns from DP methods. QT learns an action-value function and integrates a term maximizing action-values into the training loss of CSM, which aims to seek optimal actions that align closely with the behavior policy. Empirical evaluations on D4RL benchmark datasets demonstrate the superiority of QT over traditional DP and CSM methods, highlighting the potential of QT to enhance the state-of-the-art in offline RL. | [
"['Shengchao Hu' 'Ziqing Fan' 'Chaoqin Huang' 'Li Shen' 'Ya Zhang'\n 'Yanfeng Wang' 'Dacheng Tao']"
] |
null | null | 2405.17108 | null | null | http://arxiv.org/pdf/2405.17108v1 | 2024-05-27T12:24:14Z | 2024-05-27T12:24:14Z | Finding good policies in average-reward Markov Decision Processes
without prior knowledge | We revisit the identification of an $varepsilon$-optimal policy in average-reward Markov Decision Processes (MDP). In such MDPs, two measures of complexity have appeared in the literature: the diameter, $D$, and the optimal bias span, $H$, which satisfy $Hleq D$. Prior work have studied the complexity of $varepsilon$-optimal policy identification only when a generative model is available. In this case, it is known that there exists an MDP with $D simeq H$ for which the sample complexity to output an $varepsilon$-optimal policy is $Omega(SAD/varepsilon^2)$ where $S$ and $A$ are the sizes of the state and action spaces. Recently, an algorithm with a sample complexity of order $SAH/varepsilon^2$ has been proposed, but it requires the knowledge of $H$. We first show that the sample complexity required to estimate $H$ is not bounded by any function of $S,A$ and $H$, ruling out the possibility to easily make the previous algorithm agnostic to $H$. By relying instead on a diameter estimation procedure, we propose the first algorithm for $(varepsilon,delta)$-PAC policy identification that does not need any form of prior knowledge on the MDP. Its sample complexity scales in $SAD/varepsilon^2$ in the regime of small $varepsilon$, which is near-optimal. In the online setting, our first contribution is a lower bound which implies that a sample complexity polynomial in $H$ cannot be achieved in this setting. Then, we propose an online algorithm with a sample complexity in $SAD^2/varepsilon^2$, as well as a novel approach based on a data-dependent stopping rule that we believe is promising to further reduce this bound. | [
"['Adrienne Tuynman' 'Rémy Degenne' 'Emilie Kaufmann']"
] |
null | null | 2405.17111 | null | null | http://arxiv.org/pdf/2405.17111v1 | 2024-05-27T12:28:17Z | 2024-05-27T12:28:17Z | Diffusion Bridge AutoEncoders for Unsupervised Representation Learning | Diffusion-based representation learning has achieved substantial attention due to its promising capabilities in latent representation and sample generation. Recent studies have employed an auxiliary encoder to identify a corresponding representation from a sample and to adjust the dimensionality of a latent variable z. Meanwhile, this auxiliary structure invokes information split problem because the diffusion and the auxiliary encoder would divide the information from the sample into two representations for each model. Particularly, the information modeled by the diffusion becomes over-regularized because of the static prior distribution on xT. To address this problem, we introduce Diffusion Bridge AuteEncoders (DBAE), which enable z-dependent endpoint xT inference through a feed-forward architecture. This structure creates an information bottleneck at z, so xT becomes dependent on z in its generation. This results in two consequences: 1) z holds the full information of samples, and 2) xT becomes a learnable distribution, not static any further. We propose an objective function for DBAE to enable both reconstruction and generative modeling, with their theoretical justification. Empirical evidence supports the effectiveness of the intended design in DBAE, which notably enhances downstream inference quality, reconstruction, and disentanglement. Additionally, DBAE generates high-fidelity samples in the unconditional generation. | [
"['Yeongmin Kim' 'Kwanghyeon Lee' 'Minsang Park' 'Byeonghu Na'\n 'Il-Chul Moon']"
] |
null | null | 2405.17116 | null | null | http://arxiv.org/pdf/2405.17116v1 | 2024-05-27T12:33:47Z | 2024-05-27T12:33:47Z | Mixtures of Unsupervised Lexicon Classification | This paper presents a mixture version of the method-of-moment unsupervised lexicon classification by an incorporation of a Dirichlet process. | [
"['Peratham Wiriyathammabhum']"
] |
null | null | 2405.17120 | null | null | http://arxiv.org/pdf/2405.17120v1 | 2024-05-27T12:38:25Z | 2024-05-27T12:38:25Z | Dual VC Dimension Obstructs Sample Compression by Embeddings | This work studies embedding of arbitrary VC classes in well-behaved VC classes, focusing particularly on extremal classes. Our main result expresses an impossibility: such embeddings necessarily require a significant increase in dimension. In particular, we prove that for every $d$ there is a class with VC dimension $d$ that cannot be embedded in any extremal class of VC dimension smaller than exponential in $d$. In addition to its independent interest, this result has an important implication in learning theory, as it reveals a fundamental limitation of one of the most extensively studied approaches to tackling the long-standing sample compression conjecture. Concretely, the approach proposed by Floyd and Warmuth entails embedding any given VC class into an extremal class of a comparable dimension, and then applying an optimal sample compression scheme for extremal classes. However, our results imply that this strategy would in some cases result in a sample compression scheme at least exponentially larger than what is predicted by the sample compression conjecture. The above implications follow from a general result we prove: any extremal class with VC dimension $d$ has dual VC dimension at most $2d+1$. This bound is exponentially smaller than the classical bound $2^{d+1}-1$ of Assouad, which applies to general concept classes (and is known to be unimprovable for some classes). We in fact prove a stronger result, establishing that $2d+1$ upper bounds the dual Radon number of extremal classes. This theorem represents an abstraction of the classical Radon theorem for convex sets, extending its applicability to a wider combinatorial framework, without relying on the specifics of Euclidean convexity. The proof utilizes the topological method and is primarily based on variants of the Topological Radon Theorem. | [
"['Zachary Chase' 'Bogdan Chornomaz' 'Steve Hanneke' 'Shay Moran'\n 'Amir Yehudayoff']"
] |
null | null | 2405.17130 | null | null | http://arxiv.org/pdf/2405.17130v1 | 2024-05-27T12:48:30Z | 2024-05-27T12:48:30Z | Exploiting the Layered Intrinsic Dimensionality of Deep Models for
Practical Adversarial Training | Despite being a heavily researched topic, Adversarial Training (AT) is rarely, if ever, deployed in practical AI systems for two primary reasons: (i) the gained robustness is frequently accompanied by a drop in generalization and (ii) generating adversarial examples (AEs) is computationally prohibitively expensive. To address these limitations, we propose SMAAT, a new AT algorithm that leverages the manifold conjecture, stating that off-manifold AEs lead to better robustness while on-manifold AEs result in better generalization. Specifically, SMAAT aims at generating a higher proportion of off-manifold AEs by perturbing the intermediate deepnet layer with the lowest intrinsic dimension. This systematically results in better scalability compared to classical AT as it reduces the PGD chains length required for generating the AEs. Additionally, our study provides, to the best of our knowledge, the first explanation for the difference in the generalization and robustness trends between vision and language models, ie., AT results in a drop in generalization in vision models whereas, in encoder-based language models, generalization either improves or remains unchanged. We show that vision transformers and decoder-based models tend to have low intrinsic dimensionality in the earlier layers of the network (more off-manifold AEs), while encoder-based models have low intrinsic dimensionality in the later layers. We demonstrate the efficacy of SMAAT; on several tasks, including robustifying (i) sentiment classifiers, (ii) safety filters in decoder-based models, and (iii) retrievers in RAG setups. SMAAT requires only 25-33% of the GPU time compared to standard AT, while significantly improving robustness across all applications and maintaining comparable generalization. | [
"['Enes Altinisik' 'Safa Messaoud' 'Husrev Taha Sencar' 'Hassan Sajjad'\n 'Sanjay Chawla']"
] |
null | null | 2405.17132 | null | null | http://arxiv.org/pdf/2405.17132v1 | 2024-05-27T12:49:07Z | 2024-05-27T12:49:07Z | Your decision path does matter in pre-training industrial recommenders
with multi-source behaviors | Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in. Aiming at effective content delivery, cross-domain recommendation are introduced to learn high-quality representations by transferring behaviors from data-rich scenarios. However, these methods overlook the impact of the decision path that users take when conduct behaviors, that is, users ultimately exhibit different behaviors based on various intents. To this end, we propose HIER, a novel Hierarchical decIsion path Enhanced Representation learning for cross-domain recommendation. With the help of graph neural networks for high-order topological information of the knowledge graph between multi-source behaviors, we further adaptively learn decision paths through well-designed exemplar-level and information bottleneck based contrastive learning. Extensive experiments in online and offline environments show the superiority of HIER. | [
"['Chunjing Gan' 'Binbin Hu' 'Bo Huang' 'Ziqi Liu' 'Jian Ma'\n 'Zhiqiang Zhang' 'Wenliang Zhong' 'Jun Zhou']"
] |
null | null | 2405.17139 | null | null | http://arxiv.org/pdf/2405.17139v1 | 2024-05-27T12:59:35Z | 2024-05-27T12:59:35Z | Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive
Backbone Ensembling | Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various architectures, from vision transformers (ViTs) to convolutional networks (ResNets) have been trained with CLIP to serve as general solutions to diverse vision tasks. This paper explores the differences across various CLIP-trained vision backbones. Despite using the same data and training objective, we find that these architectures have notably different representations, different classification performance across datasets, and different robustness properties to certain types of image perturbations. Our findings indicate a remarkable possible synergy across backbones by leveraging their respective strengths. In principle, classification accuracy could be improved by over 40 percentage with an informed selection of the optimal backbone per test example.Using this insight, we develop a straightforward yet powerful approach to adaptively ensemble multiple backbones. The approach uses as few as one labeled example per class to tune the adaptive combination of backbones. On a large collection of datasets, the method achieves a remarkable increase in accuracy of up to 39.1% over the best single backbone, well beyond traditional ensembles | [
"['Cristian Rodriguez-Opazo' 'Ehsan Abbasnejad' 'Damien Teney'\n 'Edison Marrese-Taylor' 'Hamed Damirchi' 'Anton van den Hengel']"
] |
null | null | 2405.17151 | null | null | http://arxiv.org/pdf/2405.17151v1 | 2024-05-27T13:26:34Z | 2024-05-27T13:26:34Z | Smoke and Mirrors in Causal Downstream Tasks | Machine Learning and AI have the potential to transform data-driven scientific discovery, enabling accurate predictions for several scientific phenomena. As many scientific questions are inherently causal, this paper looks at the causal inference task of treatment effect estimation, where we assume binary effects that are recorded as high-dimensional images in a Randomized Controlled Trial (RCT). Despite being the simplest possible setting and a perfect fit for deep learning, we theoretically find that many common choices in the literature may lead to biased estimates. To test the practical impact of these considerations, we recorded the first real-world benchmark for causal inference downstream tasks on high-dimensional observations as an RCT studying how garden ants (Lasius neglectus) respond to microparticles applied onto their colony members by hygienic grooming. Comparing 6 480 models fine-tuned from state-of-the-art visual backbones, we find that the sampling and modeling choices significantly affect the accuracy of the causal estimate, and that classification accuracy is not a proxy thereof. We further validated the analysis, repeating it on a synthetically generated visual data set controlling the causal model. Our results suggest that future benchmarks should carefully consider real downstream scientific questions, especially causal ones. Further, we highlight guidelines for representation learning methods to help answer causal questions in the sciences. All code and data will be released. | [
"['Riccardo Cadei' 'Lukas Lindorfer' 'Sylvia Cremer' 'Cordelia Schmid'\n 'Francesco Locatello']"
] |
null | null | 2405.17156 | null | null | http://arxiv.org/pdf/2405.17156v2 | 2024-06-17T12:13:21Z | 2024-05-27T13:31:03Z | The Scaling Law in Stellar Light Curves | Analyzing time series of fluxes from stars, known as stellar light curves, can reveal valuable information about stellar properties. However, most current methods rely on extracting summary statistics, and studies using deep learning have been limited to supervised approaches. In this research, we investigate the scaling law properties that emerge when learning from astronomical time series data using self-supervised techniques. By employing the GPT-2 architecture, we show the learned representation improves as the number of parameters increases from $10^4$ to $10^9$, with no signs of performance plateauing. We demonstrate that a self-supervised Transformer model achieves 3-10 times the sample efficiency compared to the state-of-the-art supervised learning model when inferring the surface gravity of stars as a downstream task. Our research lays the groundwork for analyzing stellar light curves by examining them through large-scale auto-regressive generative models. | [
"['Jia-Shu Pan' 'Yuan-Sen Ting' 'Yang Huang' 'Jie Yu' 'Ji-Feng Liu']"
] |
null | null | 2405.17163 | null | null | http://arxiv.org/pdf/2405.17163v1 | 2024-05-27T13:36:50Z | 2024-05-27T13:36:50Z | Injecting Hamiltonian Architectural Bias into Deep Graph Networks for
Long-Range Propagation | The dynamics of information diffusion within graphs is a critical open issue that heavily influences graph representation learning, especially when considering long-range propagation. This calls for principled approaches that control and regulate the degree of propagation and dissipation of information throughout the neural flow. Motivated by this, we introduce (port-)Hamiltonian Deep Graph Networks, a novel framework that models neural information flow in graphs by building on the laws of conservation of Hamiltonian dynamical systems. We reconcile under a single theoretical and practical framework both non-dissipative long-range propagation and non-conservative behaviors, introducing tools from mechanical systems to gauge the equilibrium between the two components. Our approach can be applied to general message-passing architectures, and it provides theoretical guarantees on information conservation in time. Empirical results prove the effectiveness of our port-Hamiltonian scheme in pushing simple graph convolutional architectures to state-of-the-art performance in long-range benchmarks. | [
"['Simon Heilig' 'Alessio Gravina' 'Alessandro Trenta' 'Claudio Gallicchio'\n 'Davide Bacciu']"
] |
null | null | 2405.17164 | null | null | http://arxiv.org/pdf/2405.17164v2 | 2024-05-28T10:30:29Z | 2024-05-27T13:38:28Z | WeiPer: OOD Detection using Weight Perturbations of Class Projections | Recent advances in out-of-distribution (OOD) detection on image data show that pre-trained neural network classifiers can separate in-distribution (ID) from OOD data well, leveraging the class-discriminative ability of the model itself. Methods have been proposed that either use logit information directly or that process the model's penultimate layer activations. With "WeiPer", we introduce perturbations of the class projections in the final fully connected layer which creates a richer representation of the input. We show that this simple trick can improve the OOD detection performance of a variety of methods and additionally propose a distance-based method that leverages the properties of the augmented WeiPer space. We achieve state-of-the-art OOD detection results across multiple benchmarks of the OpenOOD framework, especially pronounced in difficult settings in which OOD samples are positioned close to the training set distribution. We support our findings with theoretical motivations and empirical observations, and run extensive ablations to provide insights into why WeiPer works. | [
"['Maximilian Granz' 'Manuel Heurich' 'Tim Landgraf']"
] |
null | null | 2405.17170 | null | null | http://arxiv.org/pdf/2405.17170v2 | 2024-06-14T10:10:13Z | 2024-05-27T13:49:24Z | Forecasting Four Business Cycle Phases Using Machine Learning: A Case
Study of US and EuroZone | Understanding the business cycle is crucial for building economic stability, guiding business planning, and informing investment decisions. The business cycle refers to the recurring pattern of expansion and contraction in economic activity over time. Economic analysis is inherently complex, incorporating a myriad of factors (such as macroeconomic indicators, political decisions). This complexity makes it challenging to fully account for all variables when determining the current state of the economy and predicting its future trajectory in the upcoming months. The objective of this study is to investigate the capacity of machine learning models in automatically analyzing the state of the economic, with the goal of forecasting business phases (expansion, slowdown, recession and recovery) in the United States and the EuroZone. We compared three different machine learning approaches to classify the phases of the business cycle, and among them, the Multinomial Logistic Regression (MLR) achieved the best results. Specifically, MLR got the best results by achieving the accuracy of 65.25% (Top1) and 84.74% (Top2) for the EuroZone and 75% (Top1) and 92.14% (Top2) for the United States. These results demonstrate the potential of machine learning techniques to predict business cycles accurately, which can aid in making informed decisions in the fields of economics and finance. | [
"['Elvys Linhares Pontes' 'Mohamed Benjannet' 'Raymond Yung']"
] |
null | null | 2405.17181 | null | null | http://arxiv.org/pdf/2405.17181v1 | 2024-05-27T14:01:42Z | 2024-05-27T14:01:42Z | Spectral regularization for adversarially-robust representation learning | The vulnerability of neural network classifiers to adversarial attacks is a major obstacle to their deployment in safety-critical applications. Regularization of network parameters during training can be used to improve adversarial robustness and generalization performance. Usually, the network is regularized end-to-end, with parameters at all layers affected by regularization. However, in settings where learning representations is key, such as self-supervised learning (SSL), layers after the feature representation will be discarded when performing inference. For these models, regularizing up to the feature space is more suitable. To this end, we propose a new spectral regularizer for representation learning that encourages black-box adversarial robustness in downstream classification tasks. In supervised classification settings, we show empirically that this method is more effective in boosting test accuracy and robustness than previously-proposed methods that regularize all layers of the network. We then show that this method improves the adversarial robustness of classifiers using representations learned with self-supervised training or transferred from another classification task. In all, our work begins to unveil how representational structure affects adversarial robustness. | [
"['Sheng Yang' 'Jacob A. Zavatone-Veth' 'Cengiz Pehlevan']"
] |
null | null | 2405.17198 | null | null | http://arxiv.org/pdf/2405.17198v1 | 2024-05-27T14:19:53Z | 2024-05-27T14:19:53Z | Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic
Space | Hyperbolic spaces have increasingly been recognized for their outstanding performance in handling data with inherent hierarchical structures compared to their Euclidean counterparts. However, learning in hyperbolic spaces poses significant challenges. In particular, extending support vector machines to hyperbolic spaces is in general a constrained non-convex optimization problem. Previous and popular attempts to solve hyperbolic SVMs, primarily using projected gradient descent, are generally sensitive to hyperparameters and initializations, often leading to suboptimal solutions. In this work, by first rewriting the problem into a polynomial optimization, we apply semidefinite relaxation and sparse moment-sum-of-squares relaxation to effectively approximate the optima. From extensive empirical experiments, these methods are shown to perform better than the projected gradient descent approach. | [
"['Sheng Yang' 'Peihan Liu' 'Cengiz Pehlevan']"
] |
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