categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2404.06230 | null | null | http://arxiv.org/pdf/2404.06230v1 | 2024-04-09T11:42:32Z | 2024-04-09T11:42:32Z | Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid
Byzantines in Federated Learning | Federated learning (FL) has been introduced to enable a large number of clients, possibly mobile devices, to collaborate on generating a generalized machine learning model thanks to utilizing a larger number of local samples without sharing to offer certain privacy to collaborating clients. However, due to the participation of a large number of clients, it is often difficult to profile and verify each client, which leads to a security threat that malicious participants may hamper the accuracy of the trained model by conveying poisoned models during the training. Hence, the aggregation framework at the parameter server also needs to minimize the detrimental effects of these malicious clients. A plethora of attack and defence strategies have been analyzed in the literature. However, often the Byzantine problem is analyzed solely from the outlier detection perspective, being oblivious to the topology of neural networks (NNs). In the scope of this work, we argue that by extracting certain side information specific to the NN topology, one can design stronger attacks. Hence, inspired by the sparse neural networks, we introduce a hybrid sparse Byzantine attack that is composed of two parts: one exhibiting a sparse nature and attacking only certain NN locations with higher sensitivity, and the other being more silent but accumulating over time, where each ideally targets a different type of defence mechanism, and together they form a strong but imperceptible attack. Finally, we show through extensive simulations that the proposed hybrid Byzantine attack is effective against 8 different defence methods. | [
"['Emre Ozfatura' 'Kerem Ozfatura' 'Alptekin Kupcu' 'Deniz Gunduz']"
]
|
null | null | 2404.06236 | null | null | http://arxiv.org/abs/2404.06236v1 | 2024-04-09T11:56:29Z | 2024-04-09T11:56:29Z | Towards Robust Domain Generation Algorithm Classification | In this work, we conduct a comprehensive study on the robustness of domain generation algorithm (DGA) classifiers. We implement 32 white-box attacks, 19 of which are very effective and induce a false-negative rate (FNR) of $approx$ 100% on unhardened classifiers. To defend the classifiers, we evaluate different hardening approaches and propose a novel training scheme that leverages adversarial latent space vectors and discretized adversarial domains to significantly improve robustness. In our study, we highlight a pitfall to avoid when hardening classifiers and uncover training biases that can be easily exploited by attackers to bypass detection, but which can be mitigated by adversarial training (AT). In our study, we do not observe any trade-off between robustness and performance, on the contrary, hardening improves a classifier's detection performance for known and unknown DGAs. We implement all attacks and defenses discussed in this paper as a standalone library, which we make publicly available to facilitate hardening of DGA classifiers: https://gitlab.com/rwth-itsec/robust-dga-detection | [
"['Arthur Drichel' 'Marc Meyer' 'Ulrike Meyer']"
]
|
null | null | 2404.06243 | null | null | http://arxiv.org/pdf/2404.06243v1 | 2024-04-09T12:09:56Z | 2024-04-09T12:09:56Z | ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised
Action Recognition in Videos | Human action or activity recognition in videos is a fundamental task in computer vision with applications in surveillance and monitoring, self-driving cars, sports analytics, human-robot interaction and many more. Traditional supervised methods require large annotated datasets for training, which are expensive and time-consuming to acquire. This work proposes a novel approach using Cross-Architecture Pseudo-Labeling with contrastive learning for semi-supervised action recognition. Our framework leverages both labeled and unlabelled data to robustly learn action representations in videos, combining pseudo-labeling with contrastive learning for effective learning from both types of samples. We introduce a novel cross-architecture approach where 3D Convolutional Neural Networks (3D CNNs) and video transformers (VIT) are utilised to capture different aspects of action representations; hence we call it ActNetFormer. The 3D CNNs excel at capturing spatial features and local dependencies in the temporal domain, while VIT excels at capturing long-range dependencies across frames. By integrating these complementary architectures within the ActNetFormer framework, our approach can effectively capture both local and global contextual information of an action. This comprehensive representation learning enables the model to achieve better performance in semi-supervised action recognition tasks by leveraging the strengths of each of these architectures. Experimental results on standard action recognition datasets demonstrate that our approach performs better than the existing methods, achieving state-of-the-art performance with only a fraction of labeled data. The official website of this work is available at: https://github.com/rana2149/ActNetFormer. | [
"['Sharana Dharshikgan Suresh Dass' 'Hrishav Bakul Barua'\n 'Ganesh Krishnasamy' 'Raveendran Paramesran' 'Raphael C. -W. Phan']"
]
|
null | null | 2404.06267 | null | null | http://arxiv.org/pdf/2404.06267v1 | 2024-04-09T12:45:17Z | 2024-04-09T12:45:17Z | PGTNet: A Process Graph Transformer Network for Remaining Time
Prediction of Business Process Instances | We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remaining time of business process instances. PGTNet consistently outperforms state-of-the-art deep learning approaches across a diverse range of 20 publicly available real-world event logs. Notably, our approach is most promising for highly complex processes, where existing deep learning approaches encounter difficulties stemming from their limited ability to learn control-flow relationships among process activities and capture long-range dependencies. PGTNet addresses these challenges, while also being able to consider multiple process perspectives during the learning process. | [
"['Keyvan Amiri Elyasi' 'Han van der Aa' 'Heiner Stuckenschmidt']"
]
|
null | null | 2404.06278 | null | null | http://arxiv.org/pdf/2404.06278v1 | 2024-04-09T13:02:22Z | 2024-04-09T13:02:22Z | Dimensionality Reduction in Sentence Transformer Vector Databases with
Fast Fourier Transform | Dimensionality reduction in vector databases is pivotal for streamlining AI data management, enabling efficient storage, faster computation, and improved model performance. This paper explores the benefits of reducing vector database dimensions, with a focus on computational efficiency and overcoming the curse of dimensionality. We introduce a novel application of Fast Fourier Transform (FFT) to dimensionality reduction, a method previously underexploited in this context. By demonstrating its utility across various AI domains, including Retrieval-Augmented Generation (RAG) models and image processing, this FFT-based approach promises to improve data retrieval processes and enhance the efficiency and scalability of AI solutions. The incorporation of FFT may not only optimize operations in real-time processing and recommendation systems but also extend to advanced image processing techniques, where dimensionality reduction can significantly improve performance and analysis efficiency. This paper advocates for the broader adoption of FFT in vector database management, marking a significant stride towards addressing the challenges of data volume and complexity in AI research and applications. Unlike many existing approaches, we directly handle the embedding vectors produced by the model after processing a test input. | [
"['Vitaly Bulgakov' 'Alec Segal']"
]
|
null | null | 2404.06280 | null | null | http://arxiv.org/pdf/2404.06280v2 | 2024-04-10T16:30:07Z | 2024-04-09T13:02:40Z | Algorithms for Caching and MTS with reduced number of predictions | ML-augmented algorithms utilize predictions to achieve performance beyond their worst-case bounds. Producing these predictions might be a costly operation -- this motivated Im et al. '22 to introduce the study of algorithms which use predictions parsimoniously. We design parsimonious algorithms for caching and MTS with action predictions, proposed by Antoniadis et al. '20, focusing on the parameters of consistency (performance with perfect predictions) and smoothness (dependence of their performance on the prediction error). Our algorithm for caching is 1-consistent, robust, and its smoothness deteriorates with the decreasing number of available predictions. We propose an algorithm for general MTS whose consistency and smoothness both scale linearly with the decreasing number of predictions. Without the restriction on the number of available predictions, both algorithms match the earlier guarantees achieved by Antoniadis et al. '20. | [
"['Karim Abdel Sadek' 'Marek Elias']"
]
|
null | null | 2404.06282 | null | null | http://arxiv.org/pdf/2404.06282v1 | 2024-04-09T13:08:28Z | 2024-04-09T13:08:28Z | Simple algorithms to test and learn local Hamiltonians | We consider the problems of testing and learning an $n$-qubit $k$-local Hamiltonian from queries to its evolution operator with respect the 2-norm of the Pauli spectrum, or equivalently, the normalized Frobenius norm. For testing whether a Hamiltonian is $epsilon_1$-close to $k$-local or $epsilon_2$-far from $k$-local, we show that $O(1/(epsilon_2-epsilon_1)^{8})$ queries suffice. This solves two questions posed in a recent work by Bluhm, Caro and Oufkir. For learning up to error $epsilon$, we show that $exp(O(k^2+klog(1/epsilon)))$ queries suffice. Our proofs are simple, concise and based on Pauli-analytic techniques. | [
"['Francisco Escudero Gutiérrez']"
]
|
null | null | 2404.06287 | null | null | http://arxiv.org/pdf/2404.06287v2 | 2024-06-13T03:38:36Z | 2024-04-09T13:13:24Z | Counterfactual Reasoning for Multi-Label Image Classification via
Patching-Based Training | The key to multi-label image classification (MLC) is to improve model performance by leveraging label correlations. Unfortunately, it has been shown that overemphasizing co-occurrence relationships can cause the overfitting issue of the model, ultimately leading to performance degradation. In this paper, we provide a causal inference framework to show that the correlative features caused by the target object and its co-occurring objects can be regarded as a mediator, which has both positive and negative impacts on model predictions. On the positive side, the mediator enhances the recognition performance of the model by capturing co-occurrence relationships; on the negative side, it has the harmful causal effect that causes the model to make an incorrect prediction for the target object, even when only co-occurring objects are present in an image. To address this problem, we propose a counterfactual reasoning method to measure the total direct effect, achieved by enhancing the direct effect caused only by the target object. Due to the unknown location of the target object, we propose patching-based training and inference to accomplish this goal, which divides an image into multiple patches and identifies the pivot patch that contains the target object. Experimental results on multiple benchmark datasets with diverse configurations validate that the proposed method can achieve state-of-the-art performance. | [
"['Ming-Kun Xie' 'Jia-Hao Xiao' 'Pei Peng' 'Gang Niu' 'Masashi Sugiyama'\n 'Sheng-Jun Huang']"
]
|
null | null | 2404.06294 | null | null | http://arxiv.org/pdf/2404.06294v1 | 2024-04-09T13:19:43Z | 2024-04-09T13:19:43Z | Fortifying Fully Convolutional Generative Adversarial Networks for Image
Super-Resolution Using Divergence Measures | Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) counterpart. We aim to address this by introducing Super-Resolution Generator (SuRGe), a fully-convolutional Generative Adversarial Network (GAN)-based architecture for SR. We show that distinct convolutional features obtained at increasing depths of a GAN generator can be optimally combined by a set of learnable convex weights to improve the quality of generated SR samples. In the process, we employ the Jensen-Shannon and the Gromov-Wasserstein losses respectively between the SR-HR and LR-SR pairs of distributions to further aid the generator of SuRGe to better exploit the available information in an attempt to improve SR. Moreover, we train the discriminator of SuRGe with the Wasserstein loss with gradient penalty, to primarily prevent mode collapse. The proposed SuRGe, as an end-to-end GAN workflow tailor-made for super-resolution, offers improved performance while maintaining low inference time. The efficacy of SuRGe is substantiated by its superior performance compared to 18 state-of-the-art contenders on 10 benchmark datasets. | [
"['Arkaprabha Basu' 'Kushal Bose' 'Sankha Subhra Mullick'\n 'Anish Chakrabarty' 'Swagatam Das']"
]
|
null | null | 2404.06313 | null | null | http://arxiv.org/pdf/2404.06313v2 | 2024-04-11T09:27:12Z | 2024-04-09T13:47:37Z | On adversarial training and the 1 Nearest Neighbor classifier | The ability to fool deep learning classifiers with tiny perturbations of the input has lead to the development of adversarial training in which the loss with respect to adversarial examples is minimized in addition to the training examples. While adversarial training improves the robustness of the learned classifiers, the procedure is computationally expensive, sensitive to hyperparameters and may still leave the classifier vulnerable to other types of small perturbations. In this paper we analyze the adversarial robustness of the 1 Nearest Neighbor (1NN) classifier and compare its performance to adversarial training. We prove that under reasonable assumptions, the 1 NN classifier will be robust to {em any} small image perturbation of the training images and will give high adversarial accuracy on test images as the number of training examples goes to infinity. In experiments with 45 different binary image classification problems taken from CIFAR10, we find that 1NN outperform TRADES (a powerful adversarial training algorithm) in terms of average adversarial accuracy. In additional experiments with 69 pretrained robust models for CIFAR10, we find that 1NN outperforms almost all of them in terms of robustness to perturbations that are only slightly different from those seen during training. Taken together, our results suggest that modern adversarial training methods still fall short of the robustness of the simple 1NN classifier. our code can be found at https://github.com/amirhagai/On-Adversarial-Training-And-The-1-Nearest-Neighbor-Classifier | [
"['Amir Hagai' 'Yair Weiss']"
]
|
null | null | 2404.06314 | null | null | http://arxiv.org/pdf/2404.06314v1 | 2024-04-09T13:48:53Z | 2024-04-09T13:48:53Z | Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks | Quantum computer simulation software is an integral tool for the research efforts in the quantum computing community. An important aspect is the efficiency of respective frameworks, especially for training variational quantum algorithms. Focusing on the widely used Qiskit software environment, we develop the qiskit-torch-module. It improves runtime performance by two orders of magnitude over comparable libraries, while facilitating low-overhead integration with existing codebases. Moreover, the framework provides advanced tools for integrating quantum neural networks with PyTorch. The pipeline is tailored for single-machine compute systems, which constitute a widely employed setup in day-to-day research efforts. | [
"['Nico Meyer' 'Christian Ufrecht' 'Maniraman Periyasamy' 'Axel Plinge'\n 'Christopher Mutschler' 'Daniel D. Scherer' 'Andreas Maier']"
]
|
null | null | 2404.06324 | null | null | http://arxiv.org/pdf/2404.06324v1 | 2024-04-09T14:03:04Z | 2024-04-09T14:03:04Z | Dynamic D2D-Assisted Federated Learning over O-RAN: Performance
Analysis, MAC Scheduler, and Asymmetric User Selection | Existing studies on federated learning (FL) are mostly focused on system orchestration for static snapshots of the network and making static control decisions (e.g., spectrum allocation). However, real-world wireless networks are susceptible to temporal variations of wireless channel capacity and users' datasets. In this paper, we incorporate multi-granular system dynamics (MSDs) into FL, including (M1) dynamic wireless channel capacity, captured by a set of discrete-time events, called $mathscr{D}$-Events, and (M2) dynamic datasets of users. The latter is characterized by (M2-a) modeling the dynamics of user's dataset size via an ordinary differential equation and (M2-b) introducing dynamic model drift}, formulated via a partial differential inequality} drawing concrete analytical connections between the dynamics of users' datasets and FL accuracy. We then conduct FL orchestration under MSDs by introducing dynamic cooperative FL with dedicated MAC schedulers (DCLM), exploiting the unique features of open radio access network (O-RAN). DCLM proposes (i) a hierarchical device-to-device (D2D)-assisted model training, (ii) dynamic control decisions through dedicated O-RAN MAC schedulers, and (iii) asymmetric user selection. We provide extensive theoretical analysis to study the convergence of DCLM. We then optimize the degrees of freedom (e.g., user selection and spectrum allocation) in DCLM through a highly non-convex optimization problem. We develop a systematic approach to obtain the solution for this problem, opening the door to solving a broad variety of network-aware FL optimization problems. We show the efficiency of DCLM via numerical simulations and provide a series of future directions. | [
"['Payam Abdisarabshali' 'Kwang Taik Kim' 'Michael Langberg' 'Weifeng Su'\n 'Seyyedali Hosseinalipour']"
]
|
null | null | 2404.06326 | null | null | http://arxiv.org/pdf/2404.06326v1 | 2024-04-09T14:04:26Z | 2024-04-09T14:04:26Z | What is the $\textit{intrinsic}$ dimension of your binary data? -- and
how to compute it quickly | Dimensionality is an important aspect for analyzing and understanding (high-dimensional) data. In their 2006 ICDM paper Tatti et al. answered the question for a (interpretable) dimension of binary data tables by introducing a normalized correlation dimension. In the present work we revisit their results and contrast them with a concept based notion of intrinsic dimension (ID) recently introduced for geometric data sets. To do this, we present a novel approximation for this ID that is based on computing concepts only up to a certain support value. We demonstrate and evaluate our approximation using all available datasets from Tatti et al., which have between 469 and 41271 extrinsic dimensions. | [
"['Tom Hanika' 'Tobias Hille']"
]
|
null | null | 2404.06330 | null | null | http://arxiv.org/pdf/2404.06330v1 | 2024-04-09T14:08:47Z | 2024-04-09T14:08:47Z | Generative Pre-Trained Transformer for Symbolic Regression Base
In-Context Reinforcement Learning | The mathematical formula is the human language to describe nature and is the essence of scientific research. Finding mathematical formulas from observational data is a major demand of scientific research and a major challenge of artificial intelligence. This area is called symbolic regression. Originally symbolic regression was often formulated as a combinatorial optimization problem and solved using GP or reinforcement learning algorithms. These two kinds of algorithms have strong noise robustness ability and good Versatility. However, inference time usually takes a long time, so the search efficiency is relatively low. Later, based on large-scale pre-training data proposed, such methods use a large number of synthetic data points and expression pairs to train a Generative Pre-Trained Transformer(GPT). Then this GPT can only need to perform one forward propagation to obtain the results, the advantage is that the inference speed is very fast. However, its performance is very dependent on the training data and performs poorly on data outside the training set, which leads to poor noise robustness and Versatility of such methods. So, can we combine the advantages of the above two categories of SR algorithms? In this paper, we propose textbf{FormulaGPT}, which trains a GPT using massive sparse reward learning histories of reinforcement learning-based SR algorithms as training data. After training, the SR algorithm based on reinforcement learning is distilled into a Transformer. When new test data comes, FormulaGPT can directly generate a "reinforcement learning process" and automatically update the learning policy in context. Tested on more than ten datasets including SRBench, formulaGPT achieves the state-of-the-art performance in fitting ability compared with four baselines. In addition, it achieves satisfactory results in noise robustness, versatility, and inference efficiency. | [
"['Yanjie Li' 'Weijun Li' 'Lina Yu' 'Min Wu' 'Jingyi Liu' 'Wenqiang Li'\n 'Meilan Hao' 'Shu Wei' 'Yusong Deng']"
]
|
null | null | 2404.06336 | null | null | http://arxiv.org/pdf/2404.06336v2 | 2024-05-25T23:07:29Z | 2024-04-09T14:21:51Z | Quantum State Generation with Structure-Preserving Diffusion Model | This article considers the generative modeling of the (mixed) states of quantum systems, and an approach based on denoising diffusion model is proposed. The key contribution is an algorithmic innovation that respects the physical nature of quantum states. More precisely, the commonly used density matrix representation of mixed-state has to be complex-valued Hermitian, positive semi-definite, and trace one. Generic diffusion models, or other generative methods, may not be able to generate data that strictly satisfy these structural constraints, even if all training data do. To develop a machine learning algorithm that has physics hard-wired in, we leverage mirror diffusion and borrow the physical notion of von Neumann entropy to design a new map, for enabling strict structure-preserving generation. Both unconditional generation and conditional generation via classifier-free guidance are experimentally demonstrated efficacious, the latter enabling the design of new quantum states when generated on unseen labels. | [
"['Yuchen Zhu' 'Tianrong Chen' 'Evangelos A. Theodorou' 'Xie Chen'\n 'Molei Tao']"
]
|
null | null | 2404.06339 | null | null | http://arxiv.org/pdf/2404.06339v1 | 2024-04-09T14:25:27Z | 2024-04-09T14:25:27Z | Finding fake reviews in e-commerce platforms by using hybrid algorithms | Sentiment analysis, a vital component in natural language processing, plays a crucial role in understanding the underlying emotions and opinions expressed in textual data. In this paper, we propose an innovative ensemble approach for sentiment analysis for finding fake reviews that amalgamate the predictive capabilities of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree classifiers. Our ensemble architecture strategically combines these diverse models to capitalize on their strengths while mitigating inherent weaknesses, thereby achieving superior accuracy and robustness in fake review prediction. By combining all the models of our classifiers, the predictive performance is boosted and it also fosters adaptability to varied linguistic patterns and nuances present in real-world datasets. The metrics accounted for on fake reviews demonstrate the efficacy and competitiveness of the proposed ensemble method against traditional single-model approaches. Our findings underscore the potential of ensemble techniques in advancing the state-of-the-art in finding fake reviews using hybrid algorithms, with implications for various applications in different social media and e-platforms to find the best reviews and neglect the fake ones, eliminating puffery and bluffs. | [
"['Mathivanan Periasamy' 'Rohith Mahadevan' 'Bagiya Lakshmi S'\n 'Raja CSP Raman' 'Hasan Kumar S' 'Jasper Jessiman']"
]
|
null | null | 2404.06349 | null | null | http://arxiv.org/pdf/2404.06349v1 | 2024-04-09T14:40:08Z | 2024-04-09T14:40:08Z | CausalBench: A Comprehensive Benchmark for Causal Learning Capability of
Large Language Models | Causality reveals fundamental principles behind data distributions in real-world scenarios, and the capability of large language models (LLMs) to understand causality directly impacts their efficacy across explaining outputs, adapting to new evidence, and generating counterfactuals. With the proliferation of LLMs, the evaluation of this capacity is increasingly garnering attention. However, the absence of a comprehensive benchmark has rendered existing evaluation studies being straightforward, undiversified, and homogeneous. To address these challenges, this paper proposes a comprehensive benchmark, namely CausalBench, to evaluate the causality understanding capabilities of LLMs. Originating from the causal research community, CausalBench encompasses three causal learning-related tasks, which facilitate a convenient comparison of LLMs' performance with classic causal learning algorithms. Meanwhile, causal networks of varying scales and densities are integrated in CausalBench, to explore the upper limits of LLMs' capabilities across task scenarios of varying difficulty. Notably, background knowledge and structured data are also incorporated into CausalBench to thoroughly unlock the underlying potential of LLMs for long-text comprehension and prior information utilization. Based on CausalBench, this paper evaluates nineteen leading LLMs and unveils insightful conclusions in diverse aspects. Firstly, we present the strengths and weaknesses of LLMs and quantitatively explore the upper limits of their capabilities across various scenarios. Meanwhile, we further discern the adaptability and abilities of LLMs to specific structural networks and complex chain of thought structures. Moreover, this paper quantitatively presents the differences across diverse information sources and uncovers the gap between LLMs' capabilities in causal understanding within textual contexts and numerical domains. | [
"['Yu Zhou' 'Xingyu Wu' 'Beicheng Huang' 'Jibin Wu' 'Liang Feng'\n 'Kay Chen Tan']"
]
|
null | null | 2404.06353 | null | null | http://arxiv.org/pdf/2404.06353v1 | 2024-04-09T14:44:12Z | 2024-04-09T14:44:12Z | High Noise Scheduling is a Must | Consistency models possess high capabilities for image generation, advancing sampling steps to a single step through their advanced techniques. Current advancements move one step forward consistency training techniques and eliminates the limitation of distillation training. Even though the proposed curriculum and noise scheduling in improved training techniques yield better results than basic consistency models, it lacks well balanced noise distribution and its consistency between curriculum. In this study, it is investigated the balance between high and low noise levels in noise distribution and offered polynomial noise distribution to maintain the stability. This proposed polynomial noise distribution is also supported with a predefined Karras noises to prevent unique noise levels arises with Karras noise generation algorithm. Furthermore, by elimination of learned noisy steps with a curriculum based on sinusoidal function increase the performance of the model in denoising. To make a fair comparison with the latest released consistency model training techniques, experiments are conducted with same hyper-parameters except curriculum and noise distribution. The models utilized during experiments are determined with low depth to prove the robustness of our proposed technique. The results show that the polynomial noise distribution outperforms the model trained with log-normal noise distribution, yielding a 33.54 FID score after 100,000 training steps with constant discretization steps. Additionally, the implementation of a sinusoidal-based curriculum enhances denoising performance, resulting in a FID score of 30.48. | [
"['Mahmut S. Gokmen' 'Cody Bumgardner' 'Jie Zhang' 'Ge Wang' 'Jin Chen']"
]
|
null | null | 2404.06356 | null | null | http://arxiv.org/pdf/2404.06356v1 | 2024-04-09T14:46:48Z | 2024-04-09T14:46:48Z | Policy-Guided Diffusion | In many real-world settings, agents must learn from an offline dataset gathered by some prior behavior policy. Such a setting naturally leads to distribution shift between the behavior policy and the target policy being trained - requiring policy conservatism to avoid instability and overestimation bias. Autoregressive world models offer a different solution to this by generating synthetic, on-policy experience. However, in practice, model rollouts must be severely truncated to avoid compounding error. As an alternative, we propose policy-guided diffusion. Our method uses diffusion models to generate entire trajectories under the behavior distribution, applying guidance from the target policy to move synthetic experience further on-policy. We show that policy-guided diffusion models a regularized form of the target distribution that balances action likelihood under both the target and behavior policies, leading to plausible trajectories with high target policy probability, while retaining a lower dynamics error than an offline world model baseline. Using synthetic experience from policy-guided diffusion as a drop-in substitute for real data, we demonstrate significant improvements in performance across a range of standard offline reinforcement learning algorithms and environments. Our approach provides an effective alternative to autoregressive offline world models, opening the door to the controllable generation of synthetic training data. | [
"['Matthew Thomas Jackson' 'Michael Tryfan Matthews' 'Cong Lu'\n 'Benjamin Ellis' 'Shimon Whiteson' 'Jakob Foerster']"
]
|
null | null | 2404.06371 | null | null | http://arxiv.org/pdf/2404.06371v2 | 2024-07-01T13:16:49Z | 2024-04-09T15:07:25Z | Model Generation with LLMs: From Requirements to UML Sequence Diagrams | Complementing natural language (NL) requirements with graphical models can improve stakeholders' communication and provide directions for system design. However, creating models from requirements involves manual effort. The advent of generative large language models (LLMs), ChatGPT being a notable example, offers promising avenues for automated assistance in model generation. This paper investigates the capability of ChatGPT to generate a specific type of model, i.e., UML sequence diagrams, from NL requirements. We conduct a qualitative study in which we examine the sequence diagrams generated by ChatGPT for 28 requirements documents of various types and from different domains. Observations from the analysis of the generated diagrams have systematically been captured through evaluation logs, and categorized through thematic analysis. Our results indicate that, although the models generally conform to the standard and exhibit a reasonable level of understandability, their completeness and correctness with respect to the specified requirements often present challenges. This issue is particularly pronounced in the presence of requirements smells, such as ambiguity and inconsistency. The insights derived from this study can influence the practical utilization of LLMs in the RE process, and open the door to novel RE-specific prompting strategies targeting effective model generation. | [
"['Alessio Ferrari' 'Sallam Abualhaija' 'Chetan Arora']"
]
|
null | null | 2404.06391 | null | null | http://arxiv.org/pdf/2404.06391v1 | 2024-04-09T15:35:02Z | 2024-04-09T15:35:02Z | Exploring Neural Network Landscapes: Star-Shaped and Geodesic
Connectivity | One of the most intriguing findings in the structure of neural network landscape is the phenomenon of mode connectivity: For two typical global minima, there exists a path connecting them without barrier. This concept of mode connectivity has played a crucial role in understanding important phenomena in deep learning. In this paper, we conduct a fine-grained analysis of this connectivity phenomenon. First, we demonstrate that in the overparameterized case, the connecting path can be as simple as a two-piece linear path, and the path length can be nearly equal to the Euclidean distance. This finding suggests that the landscape should be nearly convex in a certain sense. Second, we uncover a surprising star-shaped connectivity: For a finite number of typical minima, there exists a center on minima manifold that connects all of them simultaneously via linear paths. These results are provably valid for linear networks and two-layer ReLU networks under a teacher-student setup, and are empirically supported by models trained on MNIST and CIFAR-10. | [
"['Zhanran Lin' 'Puheng Li' 'Lei Wu']"
]
|
null | null | 2404.06395 | null | null | http://arxiv.org/pdf/2404.06395v3 | 2024-06-03T08:54:38Z | 2024-04-09T15:36:50Z | MiniCPM: Unveiling the Potential of Small Language Models with Scalable
Training Strategies | The burgeoning interest in developing Large Language Models (LLMs) with up to trillion parameters has been met with concerns regarding resource efficiency and practical expense, particularly given the immense cost of experimentation. This scenario underscores the importance of exploring the potential of Small Language Models (SLMs) as a resource-efficient alternative. In this context, we introduce MiniCPM, specifically the 1.2B and 2.4B non-embedding parameter variants, not only excel in their respective categories but also demonstrate capabilities on par with 7B-13B LLMs. While focusing on SLMs, our approach exhibits scalability in both model and data dimensions for future LLM research. Regarding model scaling, we employ extensive model wind tunnel experiments for stable and optimal scaling. For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation. We present an in-depth analysis of the intriguing training dynamics that occurred in the WSD LRS. With WSD LRS, we are now able to efficiently study data-model scaling law without extensive retraining experiments on both axes of model and data, from which we derive the much higher compute optimal data-model ratio than Chinchilla Optimal. Additionally, we introduce MiniCPM family, including MiniCPM-DPO, MiniCPM-MoE and MiniCPM-128K, whose excellent performance further cementing MiniCPM's foundation in diverse SLM applications. MiniCPM models are available publicly at https://github.com/OpenBMB/MiniCPM . | [
"['Shengding Hu' 'Yuge Tu' 'Xu Han' 'Chaoqun He' 'Ganqu Cui' 'Xiang Long'\n 'Zhi Zheng' 'Yewei Fang' 'Yuxiang Huang' 'Weilin Zhao' 'Xinrong Zhang'\n 'Zheng Leng Thai' 'Kaihuo Zhang' 'Chongyi Wang' 'Yuan Yao'\n 'Chenyang Zhao' 'Jie Zhou' 'Jie Cai' 'Zhongwu Zhai' 'Ning Ding'\n 'Chao Jia' 'Guoyang Zeng' 'Dahai Li' 'Zhiyuan Liu' 'Maosong Sun']"
]
|
null | null | 2404.06400 | null | null | http://arxiv.org/pdf/2404.06400v1 | 2024-04-09T15:46:00Z | 2024-04-09T15:46:00Z | Dynamic Deep Learning Based Super-Resolution For The Shallow Water
Equations | Using the nonlinear shallow water equations as benchmark, we demonstrate that a simulation with the ICON-O ocean model with a 20km resolution that is frequently corrected by a U-net-type neural network can achieve discretization errors of a simulation with 10km resolution. The network, originally developed for image-based super-resolution in post-processing, is trained to compute the difference between solutions on both meshes and is used to correct the coarse mesh every 12h. Our setup is the Galewsky test case, modeling transition of a barotropic instability into turbulent flow. We show that the ML-corrected coarse resolution run correctly maintains a balance flow and captures the transition to turbulence in line with the higher resolution simulation. After 8 day of simulation, the $L_2$-error of the corrected run is similar to a simulation run on the finer mesh. While mass is conserved in the corrected runs, we observe some spurious generation of kinetic energy. | [
"['Maximilian Witte' 'Fabricio Rodrigues Lapolli' 'Philip Freese'\n 'Sebastian Götschel' 'Daniel Ruprecht' 'Peter Korn' 'Christopher Kadow']"
]
|
null | null | 2404.06403 | null | null | http://arxiv.org/pdf/2404.06403v1 | 2024-04-09T15:53:02Z | 2024-04-09T15:53:02Z | Online Learning of Decision Trees with Thompson Sampling | Decision Trees are prominent prediction models for interpretable Machine Learning. They have been thoroughly researched, mostly in the batch setting with a fixed labelled dataset, leading to popular algorithms such as C4.5, ID3 and CART. Unfortunately, these methods are of heuristic nature, they rely on greedy splits offering no guarantees of global optimality and often leading to unnecessarily complex and hard-to-interpret Decision Trees. Recent breakthroughs addressed this suboptimality issue in the batch setting, but no such work has considered the online setting with data arriving in a stream. To this end, we devise a new Monte Carlo Tree Search algorithm, Thompson Sampling Decision Trees (TSDT), able to produce optimal Decision Trees in an online setting. We analyse our algorithm and prove its almost sure convergence to the optimal tree. Furthermore, we conduct extensive experiments to validate our findings empirically. The proposed TSDT outperforms existing algorithms on several benchmarks, all while presenting the practical advantage of being tailored to the online setting. | [
"['Ayman Chaouki' 'Jesse Read' 'Albert Bifet']"
]
|
null | null | 2404.06404 | null | null | http://arxiv.org/pdf/2404.06404v1 | 2024-04-09T15:53:06Z | 2024-04-09T15:53:06Z | Apprentices to Research Assistants: Advancing Research with Large
Language Models | Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and efficiency, challenges such as prompt tuning, biases, and subjectivity must be addressed. The study presents insights from experiments utilizing LLMs for qualitative analysis, highlighting successes and limitations. Additionally, it discusses strategies for mitigating challenges, such as prompt optimization techniques and leveraging human expertise. This study aligns with the 'LLMs as Research Tools' workshop's focus on integrating LLMs into HCI data work critically and ethically. By addressing both opportunities and challenges, our work contributes to the ongoing dialogue on their responsible application in research. | [
"['M. Namvarpour' 'A. Razi']"
]
|
null | null | 2404.06405 | null | null | http://arxiv.org/pdf/2404.06405v2 | 2024-04-11T14:37:29Z | 2024-04-09T15:54:00Z | Wu's Method can Boost Symbolic AI to Rival Silver Medalists and
AlphaGeometry to Outperform Gold Medalists at IMO Geometry | Proving geometric theorems constitutes a hallmark of visual reasoning combining both intuitive and logical skills. Therefore, automated theorem proving of Olympiad-level geometry problems is considered a notable milestone in human-level automated reasoning. The introduction of AlphaGeometry, a neuro-symbolic model trained with 100 million synthetic samples, marked a major breakthrough. It solved 25 of 30 International Mathematical Olympiad (IMO) problems whereas the reported baseline based on Wu's method solved only ten. In this note, we revisit the IMO-AG-30 Challenge introduced with AlphaGeometry, and find that Wu's method is surprisingly strong. Wu's method alone can solve 15 problems, and some of them are not solved by any of the other methods. This leads to two key findings: (i) Combining Wu's method with the classic synthetic methods of deductive databases and angle, ratio, and distance chasing solves 21 out of 30 methods by just using a CPU-only laptop with a time limit of 5 minutes per problem. Essentially, this classic method solves just 4 problems less than AlphaGeometry and establishes the first fully symbolic baseline strong enough to rival the performance of an IMO silver medalist. (ii) Wu's method even solves 2 of the 5 problems that AlphaGeometry failed to solve. Thus, by combining AlphaGeometry with Wu's method we set a new state-of-the-art for automated theorem proving on IMO-AG-30, solving 27 out of 30 problems, the first AI method which outperforms an IMO gold medalist. | [
"['Shiven Sinha' 'Ameya Prabhu' 'Ponnurangam Kumaraguru' 'Siddharth Bhat'\n 'Matthias Bethge']"
]
|
null | null | 2404.06407 | null | null | http://arxiv.org/pdf/2404.06407v3 | 2024-05-07T14:06:23Z | 2024-04-09T15:54:16Z | Rethinking How to Evaluate Language Model Jailbreak | Large language models (LLMs) have become increasingly integrated with various applications. To ensure that LLMs do not generate unsafe responses, they are aligned with safeguards that specify what content is restricted. However, such alignment can be bypassed to produce prohibited content using a technique commonly referred to as jailbreak. Different systems have been proposed to perform the jailbreak automatically. These systems rely on evaluation methods to determine whether a jailbreak attempt is successful. However, our analysis reveals that current jailbreak evaluation methods have two limitations. (1) Their objectives lack clarity and do not align with the goal of identifying unsafe responses. (2) They oversimplify the jailbreak result as a binary outcome, successful or not. In this paper, we propose three metrics, safeguard violation, informativeness, and relative truthfulness, to evaluate language model jailbreak. Additionally, we demonstrate how these metrics correlate with the goal of different malicious actors. To compute these metrics, we introduce a multifaceted approach that extends the natural language generation evaluation method after preprocessing the response. We evaluate our metrics on a benchmark dataset produced from three malicious intent datasets and three jailbreak systems. The benchmark dataset is labeled by three annotators. We compare our multifaceted approach with three existing jailbreak evaluation methods. Experiments demonstrate that our multifaceted evaluation outperforms existing methods, with F1 scores improving on average by 17% compared to existing baselines. Our findings motivate the need to move away from the binary view of the jailbreak problem and incorporate a more comprehensive evaluation to ensure the safety of the language model. | [
"['Hongyu Cai' 'Arjun Arunasalam' 'Leo Y. Lin' 'Antonio Bianchi'\n 'Z. Berkay Celik']"
]
|
null | null | 2404.06418 | null | null | http://arxiv.org/pdf/2404.06418v1 | 2024-04-09T16:07:35Z | 2024-04-09T16:07:35Z | Studying the Impact of Latent Representations in Implicit Neural
Networks for Scientific Continuous Field Reconstruction | Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present a novel model called MMGN (Multiplicative and Modulated Gabor Network) with implicit neural networks. In this work, we design additional studies leveraging explainability methods to complement the previous experiments and further enhance the understanding of latent representations generated by the model. The adopted methods are general enough to be leveraged for any latent space inspection. Preliminary results demonstrate the contextual information incorporated in the latent representations and their impact on the model performance. As a work in progress, we will continue to verify our findings and develop novel explainability approaches. | [
"['Wei Xu' 'Derek Freeman DeSantis' 'Xihaier Luo' 'Avish Parmar'\n 'Klaus Tan' 'Balu Nadiga' 'Yihui Ren' 'Shinjae Yoo']"
]
|
null | null | 2404.06421 | null | null | http://arxiv.org/abs/2404.06421v3 | 2024-06-19T00:21:50Z | 2024-04-09T16:10:39Z | Efficient Training of Probabilistic Neural Networks for Survival
Analysis | Variational Inference (VI) is a commonly used technique for approximate Bayesian inference and uncertainty estimation in deep learning models, yet it comes at a computational cost, as it doubles the number of trainable parameters to represent uncertainty. This rapidly becomes challenging in high-dimensional settings and motivates the use of alternative techniques for inference, such as Monte Carlo Dropout (MCD) or Spectral-normalized Neural Gaussian Process (SNGP). However, such methods have seen little adoption in survival analysis, and VI remains the prevalent approach for training probabilistic neural networks. In this paper, we investigate how to train deep probabilistic survival models in large datasets without introducing additional overhead in model complexity. To achieve this, we adopt three probabilistic approaches, namely VI, MCD, and SNGP, and evaluate them in terms of their prediction performance, calibration performance, and model complexity. In the context of probabilistic survival analysis, we investigate whether non-VI techniques can offer comparable or possibly improved prediction performance and uncertainty calibration compared to VI. In the MIMIC-IV dataset, we find that MCD aligns with VI in terms of the concordance index (0.748 vs. 0.743) and mean absolute error (254.9 vs. 254.7) using hinge loss, while providing C-calibrated uncertainty estimates. Moreover, our SNGP implementation provides D-calibrated survival functions in all datasets compared to VI (4/4 vs. 2/4, respectively). Our work encourages the use of techniques alternative to VI for survival analysis in high-dimensional datasets, where computational efficiency and overhead are of concern. | [
"['Christian Marius Lillelund' 'Martin Magris' 'Christian Fischer Pedersen']"
]
|
null | null | 2404.06423 | null | null | http://arxiv.org/pdf/2404.06423v3 | 2024-05-03T02:05:20Z | 2024-04-09T16:14:03Z | Deep Reinforcement Learning-Based Approach for a Single Vehicle
Persistent Surveillance Problem with Fuel Constraints | This article presents a deep reinforcement learning-based approach to tackle a persistent surveillance mission requiring a single unmanned aerial vehicle initially stationed at a depot with fuel or time-of-flight constraints to repeatedly visit a set of targets with equal priority. Owing to the vehicle's fuel or time-of-flight constraints, the vehicle must be regularly refueled, or its battery must be recharged at the depot. The objective of the problem is to determine an optimal sequence of visits to the targets that minimizes the maximum time elapsed between successive visits to any target while ensuring that the vehicle never runs out of fuel or charge. We present a deep reinforcement learning algorithm to solve this problem and present the results of numerical experiments that corroborate the effectiveness of this approach in comparison with common-sense greedy heuristics. | [
"['Manav Mishra' 'Hritik Bana' 'Saswata Sarkar' 'Sujeevraja Sanjeevi'\n 'PB Sujit' 'Kaarthik Sundar']"
]
|
null | null | 2404.06430 | null | null | http://arxiv.org/pdf/2404.06430v1 | 2024-04-09T16:23:01Z | 2024-04-09T16:23:01Z | pfl-research: simulation framework for accelerating research in Private
Federated Learning | Federated learning (FL) is an emerging machine learning (ML) training paradigm where clients own their data and collaborate to train a global model, without revealing any data to the server and other participants. Researchers commonly perform experiments in a simulation environment to quickly iterate on ideas. However, existing open-source tools do not offer the efficiency required to simulate FL on larger and more realistic FL datasets. We introduce pfl-research, a fast, modular, and easy-to-use Python framework for simulating FL. It supports TensorFlow, PyTorch, and non-neural network models, and is tightly integrated with state-of-the-art privacy algorithms. We study the speed of open-source FL frameworks and show that pfl-research is 7-72$times$ faster than alternative open-source frameworks on common cross-device setups. Such speedup will significantly boost the productivity of the FL research community and enable testing hypotheses on realistic FL datasets that were previously too resource intensive. We release a suite of benchmarks that evaluates an algorithm's overall performance on a diverse set of realistic scenarios. The code is available on GitHub at https://github.com/apple/pfl-research. | [
"['Filip Granqvist' 'Congzheng Song' 'Áine Cahill' 'Rogier van Dalen'\n 'Martin Pelikan' 'Yi Sheng Chan' 'Xiaojun Feng' 'Natarajan Krishnaswami'\n 'Vojta Jina' 'Mona Chitnis']"
]
|
null | null | 2404.06437 | null | null | http://arxiv.org/pdf/2404.06437v1 | 2024-04-09T16:28:54Z | 2024-04-09T16:28:54Z | Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks | With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation. In this study, we utilize SeasFire, a comprehensive global wildfire dataset with climate, vegetation, oceanic indices, and human-related variables, to enable seasonal wildfire forecasting with machine learning. For the predictive analysis, we train deep learning models with different architectures that capture the spatio-temporal context leading to wildfires. Our investigation focuses on assessing the effectiveness of these models in predicting the presence of burned areas at varying forecasting time horizons globally, extending up to six months into the future, and on how different spatial or/and temporal context affects the performance of the models. Our findings demonstrate the great potential of deep learning models in seasonal fire forecasting; longer input time-series leads to more robust predictions across varying forecasting horizons, while integrating spatial information to capture wildfire spatio-temporal dynamics boosts performance. Finally, our results hint that in order to enhance performance at longer forecasting horizons, a larger receptive field spatially needs to be considered. | [
"['Dimitrios Michail' 'Lefki-Ioanna Panagiotou' 'Charalampos Davalas'\n 'Ioannis Prapas' 'Spyros Kondylatos' 'Nikolaos Ioannis Bountos'\n 'Ioannis Papoutsis']"
]
|
null | null | 2404.06448 | null | null | http://arxiv.org/pdf/2404.06448v1 | 2024-04-09T16:50:30Z | 2024-04-09T16:50:30Z | Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of
Large Language Models | Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). However, for many downstream tasks, it is necessary to fine-tune LLMs using private data. While federated learning offers a promising privacy-preserving solution to LLM fine-tuning, the substantial size of an LLM, combined with high computational and communication demands, makes it hard to apply to downstream tasks. More importantly, private edge servers often possess varying computing and network resources in real-world scenarios, introducing additional complexities to LLM fine-tuning. To tackle these problems, we design and implement an automated federated pipeline, named FedPipe, to fine-tune LLMs with minimal training cost but without adding any inference latency. FedPipe firstly identifies the weights to be fine-tuned based on their contributions to the LLM training. It then configures a low-rank adapter for each selected weight to train local low-rank adapters on an edge server, and aggregate local adapters of all edge servers to fine-tune the whole LLM. Finally, it appropriately quantizes the parameters of LLM to reduce memory space according to the requirements of edge servers. Extensive experiments demonstrate that FedPipe expedites the model training and achieves higher accuracy than state-of-the-art benchmarks. | [
"['Zihan Fang' 'Zheng Lin' 'Zhe Chen' 'Xianhao Chen' 'Yue Gao'\n 'Yuguang Fang']"
]
|
null | null | 2404.06453 | null | null | http://arxiv.org/pdf/2404.06453v1 | 2024-04-09T16:54:19Z | 2024-04-09T16:54:19Z | PURE: Turning Polysemantic Neurons Into Pure Features by Identifying
Relevant Circuits | The field of mechanistic interpretability aims to study the role of individual neurons in Deep Neural Networks. Single neurons, however, have the capability to act polysemantically and encode for multiple (unrelated) features, which renders their interpretation difficult. We present a method for disentangling polysemanticity of any Deep Neural Network by decomposing a polysemantic neuron into multiple monosemantic "virtual" neurons. This is achieved by identifying the relevant sub-graph ("circuit") for each "pure" feature. We demonstrate how our approach allows us to find and disentangle various polysemantic units of ResNet models trained on ImageNet. While evaluating feature visualizations using CLIP, our method effectively disentangles representations, improving upon methods based on neuron activations. Our code is available at https://github.com/maxdreyer/PURE. | [
"['Maximilian Dreyer' 'Erblina Purelku' 'Johanna Vielhaben'\n 'Wojciech Samek' 'Sebastian Lapuschkin']"
]
|
null | null | 2404.06455 | null | null | http://arxiv.org/pdf/2404.06455v1 | 2024-04-09T16:55:23Z | 2024-04-09T16:55:23Z | A comparative analysis of deep learning models for lung segmentation on
X-ray images | Robust and highly accurate lung segmentation in X-rays is crucial in medical imaging. This study evaluates deep learning solutions for this task, ranking existing methods and analyzing their performance under diverse image modifications. Out of 61 analyzed papers, only nine offered implementation or pre-trained models, enabling assessment of three prominent methods: Lung VAE, TransResUNet, and CE-Net. The analysis revealed that CE-Net performs best, demonstrating the highest values in dice similarity coefficient and intersection over union metric. | [
"['Weronika Hryniewska-Guzik' 'Jakub Bilski' 'Bartosz Chrostowski'\n 'Jakub Drak Sbahi' 'Przemysław Biecek']"
]
|
null | null | 2404.06460 | null | null | http://arxiv.org/pdf/2404.06460v2 | 2024-05-28T00:53:07Z | 2024-04-09T17:00:43Z | Learning Locally Interacting Discrete Dynamical Systems: Towards
Data-Efficient and Scalable Prediction | Locally interacting dynamical systems, such as epidemic spread, rumor propagation through crowd, and forest fire, exhibit complex global dynamics originated from local, relatively simple, and often stochastic interactions between dynamic elements. Their temporal evolution is often driven by transitions between a finite number of discrete states. Despite significant advancements in predictive modeling through deep learning, such interactions among many elements have rarely explored as a specific domain for predictive modeling. We present Attentive Recurrent Neural Cellular Automata (AR-NCA), to effectively discover unknown local state transition rules by associating the temporal information between neighboring cells in a permutation-invariant manner. AR-NCA exhibits the superior generalizability across various system configurations (i.e., spatial distribution of states), data efficiency and robustness in extremely data-limited scenarios even in the presence of stochastic interactions, and scalability through spatial dimension-independent prediction. | [
"['Beomseok Kang' 'Harshit Kumar' 'Minah Lee' 'Biswadeep Chakraborty'\n 'Saibal Mukhopadhyay']"
]
|
null | null | 2404.06466 | null | null | http://arxiv.org/pdf/2404.06466v1 | 2024-04-09T17:14:41Z | 2024-04-09T17:14:41Z | Hyperparameter Selection in Continual Learning | In continual learning (CL) -- where a learner trains on a stream of data -- standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all of the data at the same time. This has prompted the development of CL-specific HPO frameworks. The most popular way to tune hyperparameters in CL is to repeatedly train over the whole data stream with different hyperparameter settings. However, this end-of-training HPO is unrealistic as in practice a learner can only see the stream once. Hence, there is an open question: what HPO framework should a practitioner use for a CL problem in reality? This paper answers this question by evaluating several realistic HPO frameworks. We find that all the HPO frameworks considered, including end-of-training HPO, perform similarly. We therefore advocate using the realistic and most computationally efficient method: fitting the hyperparameters on the first task and then fixing them throughout training. | [
"['Thomas L. Lee' 'Sigrid Passano Hellan' 'Linus Ericsson'\n 'Elliot J. Crowley' 'Amos Storkey']"
]
|
null | null | 2404.06470 | null | null | http://arxiv.org/pdf/2404.06470v1 | 2024-04-09T17:17:48Z | 2024-04-09T17:17:48Z | Learning State-Invariant Representations of Objects from Image
Collections with State, Pose, and Viewpoint Changes | We add one more invariance - state invariance - to the more commonly used other invariances for learning object representations for recognition and retrieval. By state invariance, we mean robust with respect to changes in the structural form of the object, such as when an umbrella is folded, or when an item of clothing is tossed on the floor. Since humans generally have no difficulty in recognizing objects despite such state changes, we are naturally faced with the question of whether it is possible to devise a neural architecture with similar abilities. To that end, we present a novel dataset, ObjectsWithStateChange, that captures state and pose variations in the object images recorded from arbitrary viewpoints. We believe that this dataset will facilitate research in fine-grained object recognition and retrieval of objects that are capable of state changes. The goal of such research would be to train models capable of generating object embeddings that remain invariant to state changes while also staying invariant to transformations induced by changes in viewpoint, pose, illumination, etc. To demonstrate the usefulness of the ObjectsWithStateChange dataset, we also propose a curriculum learning strategy that uses the similarity relationships in the learned embedding space after each epoch to guide the training process. The model learns discriminative features by comparing visually similar objects within and across different categories, encouraging it to differentiate between objects that may be challenging to distinguish due to changes in their state. We believe that this strategy enhances the model's ability to capture discriminative features for fine-grained tasks that may involve objects with state changes, leading to performance improvements on object-level tasks not only on our new dataset, but also on two other challenging multi-view datasets such as ModelNet40 and ObjectPI. | [
"['Rohan Sarkar' 'Avinash Kak']"
]
|
null | null | 2404.06481 | null | null | http://arxiv.org/pdf/2404.06481v1 | 2024-04-09T17:31:18Z | 2024-04-09T17:31:18Z | GeoDirDock: Guiding Docking Along Geodesic Paths | This work introduces GeoDirDock (GDD), a novel approach to molecular docking that enhances the accuracy and physical plausibility of ligand docking predictions. GDD guides the denoising process of a diffusion model along geodesic paths within multiple spaces representing translational, rotational, and torsional degrees of freedom. Our method leverages expert knowledge to direct the generative modeling process, specifically targeting desired protein-ligand interaction regions. We demonstrate that GDD significantly outperforms existing blind docking methods in terms of RMSD accuracy and physicochemical pose realism. Our results indicate that incorporating domain expertise into the diffusion process leads to more biologically relevant docking predictions. Additionally, we explore the potential of GDD for lead optimization in drug discovery through angle transfer in maximal common substructure (MCS) docking, showcasing its capability to predict ligand orientations for chemically similar compounds accurately. | [
"['Raúl Miñán' 'Javier Gallardo' 'Álvaro Ciudad' 'Alexis Molina']"
]
|
null | null | 2404.06484 | null | null | http://arxiv.org/pdf/2404.06484v5 | 2024-05-03T15:57:04Z | 2024-04-09T17:35:11Z | Public-private funding models in open source software development: A
case study on scikit-learn | Governments are increasingly funding open source software (OSS) development to support software security, digital sovereignty, and national competitiveness in science and innovation, amongst others. However, little is known about how OSS developers evaluate the relative benefits and drawbacks of governmental funding for OSS. This study explores this question through a case study on scikit-learn, a Python library for machine learning, funded by public research grants, commercial sponsorship, micro-donations, and a 32 euro million grant announced in France's artificial intelligence strategy. Through 25 interviews with scikit-learn's maintainers and funders, this study makes two key contributions. First, it contributes empirical findings about the benefits and drawbacks of public and private funding in an impactful OSS project, and the governance protocols employed by the maintainers to balance the diverse interests of their community and funders. Second, it offers practical lessons on funding for OSS developers, governments, and companies based on the experience of scikit-learn. The paper concludes with key recommendations for practitioners and future research directions. | [
"['Cailean Osborne']"
]
|
null | null | 2404.06486 | null | null | http://arxiv.org/pdf/2404.06486v1 | 2024-04-09T17:37:08Z | 2024-04-09T17:37:08Z | GO4Align: Group Optimization for Multi-Task Alignment | This paper proposes textit{GO4Align}, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks. To achieve this, we design an adaptive group risk minimization strategy, compromising two crucial techniques in implementation: (i) dynamical group assignment, which clusters similar tasks based on task interactions; (ii) risk-guided group indicators, which exploit consistent task correlations with risk information from previous iterations. Comprehensive experimental results on diverse typical benchmarks demonstrate our method's performance superiority with even lower computational costs. | [
"['Jiayi Shen' 'Cheems Wang' 'Zehao Xiao' 'Nanne Van Noord'\n 'Marcel Worring']"
]
|
null | null | 2404.06492 | null | null | http://arxiv.org/pdf/2404.06492v1 | 2024-04-09T17:45:25Z | 2024-04-09T17:45:25Z | Graph Reinforcement Learning for Combinatorial Optimization: A Survey
and Unifying Perspective | Graphs are a natural representation for systems based on relations between connected entities. Combinatorial optimization problems, which arise when considering an objective function related to a process of interest on discrete structures, are often challenging due to the rapid growth of the solution space. The trial-and-error paradigm of Reinforcement Learning has recently emerged as a promising alternative to traditional methods, such as exact algorithms and (meta)heuristics, for discovering better decision-making strategies in a variety of disciplines including chemistry, computer science, and statistics. Despite the fact that they arose in markedly different fields, these techniques share significant commonalities. Therefore, we set out to synthesize this work in a unifying perspective that we term Graph Reinforcement Learning, interpreting it as a constructive decision-making method for graph problems. After covering the relevant technical background, we review works along the dividing line of whether the goal is to optimize graph structure given a process of interest, or to optimize the outcome of the process itself under fixed graph structure. Finally, we discuss the common challenges facing the field and open research questions. In contrast with other surveys, the present work focuses on non-canonical graph problems for which performant algorithms are typically not known and Reinforcement Learning is able to provide efficient and effective solutions. | [
"['Victor-Alexandru Darvariu' 'Stephen Hailes' 'Mirco Musolesi']"
]
|
null | null | 2404.06498 | null | null | http://arxiv.org/pdf/2404.06498v1 | 2024-04-09T17:50:38Z | 2024-04-09T17:50:38Z | Simultaneous linear connectivity of neural networks modulo permutation | Neural networks typically exhibit permutation symmetries which contribute to the non-convexity of the networks' loss landscapes, since linearly interpolating between two permuted versions of a trained network tends to encounter a high loss barrier. Recent work has argued that permutation symmetries are the only sources of non-convexity, meaning there are essentially no such barriers between trained networks if they are permuted appropriately. In this work, we refine these arguments into three distinct claims of increasing strength. We show that existing evidence only supports "weak linear connectivity"-that for each pair of networks belonging to a set of SGD solutions, there exist (multiple) permutations that linearly connect it with the other networks. In contrast, the claim "strong linear connectivity"-that for each network, there exists one permutation that simultaneously connects it with the other networks-is both intuitively and practically more desirable. This stronger claim would imply that the loss landscape is convex after accounting for permutation, and enable linear interpolation between three or more independently trained models without increased loss. In this work, we introduce an intermediate claim-that for certain sequences of networks, there exists one permutation that simultaneously aligns matching pairs of networks from these sequences. Specifically, we discover that a single permutation aligns sequences of iteratively trained as well as iteratively pruned networks, meaning that two networks exhibit low loss barriers at each step of their optimization and sparsification trajectories respectively. Finally, we provide the first evidence that strong linear connectivity may be possible under certain conditions, by showing that barriers decrease with increasing network width when interpolating among three networks. | [
"['Ekansh Sharma' 'Devin Kwok' 'Tom Denton' 'Daniel M. Roy' 'David Rolnick'\n 'Gintare Karolina Dziugaite']"
]
|
null | null | 2404.06508 | null | null | http://arxiv.org/pdf/2404.06508v2 | 2024-05-02T23:29:56Z | 2024-04-09T17:57:29Z | On the Effect of (Near) Duplicate Subwords in Language Modelling | Tokenisation is a core part of language models (LMs). It involves splitting a character sequence into subwords which are assigned arbitrary indices before being served to the LM. While typically lossless, however, this process may lead to less sample efficient LM training: as it removes character-level information, it could make it harder for LMs to generalise across similar subwords, such as now and Now. We refer to such subwords as near duplicates. In this paper, we study the impact of near duplicate subwords on LM training efficiency. First, we design an experiment that gives us an upper bound to how much we should expect a model to improve if we could perfectly generalise across near duplicates. We do this by duplicating each subword in our LM's vocabulary, creating perfectly equivalent classes of subwords. Experimentally, we find that LMs need roughly 17% more data when trained in a fully duplicated setting. Second, we investigate the impact of naturally occurring near duplicates on LMs. Here, we see that merging them considerably hurts LM performance. Therefore, although subword duplication negatively impacts LM training efficiency, naturally occurring near duplicates may not be as similar as anticipated, limiting the potential for performance improvements. | [
"['Anton Schäfer' 'Thomas Hofmann' 'Imanol Schlag' 'Tiago Pimentel']"
]
|
null | null | 2404.06511 | null | null | http://arxiv.org/pdf/2404.06511v1 | 2024-04-09T17:59:31Z | 2024-04-09T17:59:31Z | MoReVQA: Exploring Modular Reasoning Models for Video Question Answering | This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage, modular reasoning framework. Previous modular methods have shown promise with a single planning stage ungrounded in visual content. However, through a simple and effective baseline, we find that such systems can lead to brittle behavior in practice for challenging videoQA settings. Thus, unlike traditional single-stage planning methods, we propose a multi-stage system consisting of an event parser, a grounding stage, and a final reasoning stage in conjunction with an external memory. All stages are training-free, and performed using few-shot prompting of large models, creating interpretable intermediate outputs at each stage. By decomposing the underlying planning and task complexity, our method, MoReVQA, improves over prior work on standard videoQA benchmarks (NExT-QA, iVQA, EgoSchema, ActivityNet-QA) with state-of-the-art results, and extensions to related tasks (grounded videoQA, paragraph captioning). | [
"['Juhong Min' 'Shyamal Buch' 'Arsha Nagrani' 'Minsu Cho' 'Cordelia Schmid']"
]
|
null | null | 2404.06516 | null | null | http://arxiv.org/pdf/2404.06516v1 | 2024-04-04T01:02:03Z | 2024-04-04T01:02:03Z | Convergence to Nash Equilibrium and No-regret Guarantee in (Markov)
Potential Games | In this work, we study potential games and Markov potential games under stochastic cost and bandit feedback. We propose a variant of the Frank-Wolfe algorithm with sufficient exploration and recursive gradient estimation, which provably converges to the Nash equilibrium while attaining sublinear regret for each individual player. Our algorithm simultaneously achieves a Nash regret and a regret bound of $O(T^{4/5})$ for potential games, which matches the best available result, without using additional projection steps. Through carefully balancing the reuse of past samples and exploration of new samples, we then extend the results to Markov potential games and improve the best available Nash regret from $O(T^{5/6})$ to $O(T^{4/5})$. Moreover, our algorithm requires no knowledge of the game, such as the distribution mismatch coefficient, which provides more flexibility in its practical implementation. Experimental results corroborate our theoretical findings and underscore the practical effectiveness of our method. | [
"['Jing Dong' 'Baoxiang Wang' 'Yaoliang Yu']"
]
|
null | null | 2404.06517 | null | null | http://arxiv.org/pdf/2404.06517v1 | 2024-04-04T05:24:22Z | 2024-04-04T05:24:22Z | DiffObs: Generative Diffusion for Global Forecasting of Satellite
Observations | This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics. The model is trained to probabilistically forecast day-ahead precipitation. Nonetheless, it is stable for multi-month rollouts, which reveal a qualitatively realistic superposition of convectively coupled wave modes in the tropics. Cross-spectral analysis confirms successful generation of low frequency variations associated with the Madden--Julian oscillation, which regulates most subseasonal to seasonal predictability in the observed atmosphere, and convectively coupled moist Kelvin waves with approximately correct dispersion relationships. Despite secondary issues and biases, the results affirm the potential for a next generation of global diffusion models trained on increasingly sparse, and increasingly direct and differentiated observations of the world, for practical applications in subseasonal and climate prediction. | [
"['Jason Stock' 'Jaideep Pathak' 'Yair Cohen' 'Mike Pritchard'\n 'Piyush Garg' 'Dale Durran' 'Morteza Mardani' 'Noah Brenowitz']"
]
|
null | null | 2404.06519 | null | null | http://arxiv.org/pdf/2404.06519v1 | 2024-04-05T22:03:35Z | 2024-04-05T22:03:35Z | Best Response Shaping | We investigate the challenge of multi-agent deep reinforcement learning in partially competitive environments, where traditional methods struggle to foster reciprocity-based cooperation. LOLA and POLA agents learn reciprocity-based cooperative policies by differentiation through a few look-ahead optimization steps of their opponent. However, there is a key limitation in these techniques. Because they consider a few optimization steps, a learning opponent that takes many steps to optimize its return may exploit them. In response, we introduce a novel approach, Best Response Shaping (BRS), which differentiates through an opponent approximating the best response, termed the "detective." To condition the detective on the agent's policy for complex games we propose a state-aware differentiable conditioning mechanism, facilitated by a question answering (QA) method that extracts a representation of the agent based on its behaviour on specific environment states. To empirically validate our method, we showcase its enhanced performance against a Monte Carlo Tree Search (MCTS) opponent, which serves as an approximation to the best response in the Coin Game. This work expands the applicability of multi-agent RL in partially competitive environments and provides a new pathway towards achieving improved social welfare in general sum games. | [
"['Milad Aghajohari' 'Tim Cooijmans' 'Juan Agustin Duque'\n 'Shunichi Akatsuka' 'Aaron Courville']"
]
|
null | null | 2404.06535 | null | null | http://arxiv.org/pdf/2404.06535v1 | 2024-04-09T18:00:01Z | 2024-04-09T18:00:01Z | Learning to rank quantum circuits for hardware-optimized performance
enhancement | We introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits based on expected performance estimates derived from a training procedure conducted on real hardware. We apply our method to the problem of layout selection, in which abstracted qubits are assigned to physical qubits on a given device. Circuit measurements performed on IBM hardware indicate that the maximum and median fidelities of logically equivalent layouts can differ by an order of magnitude. We introduce a circuit score used for ranking that is parameterized in terms of a physics-based, phenomenological error model whose parameters are fit by training a ranking-loss function over a measured dataset. The dataset consists of quantum circuits exhibiting a diversity of structures and executed on IBM hardware, allowing the model to incorporate the contextual nature of real device noise and errors without the need to perform an exponentially costly tomographic protocol. We perform model training and execution on the 16-qubit ibmq_guadalupe device and compare our method to two common approaches: random layout selection and a publicly available baseline called Mapomatic. Our model consistently outperforms both approaches, predicting layouts that exhibit lower noise and higher performance. In particular, we find that our best model leads to a $1.8times$ reduction in selection error when compared to the baseline approach and a $3.2times$ reduction when compared to random selection. Beyond delivering a new form of predictive quantum characterization, verification, and validation, our results reveal the specific way in which context-dependent and coherent gate errors appear to dominate the divergence from performance estimates extrapolated from simple proxy measures. | [
"['Gavin S. Hartnett' 'Aaron Barbosa' 'Pranav S. Mundada' 'Michael Hush'\n 'Michael J. Biercuk' 'Yuval Baum']"
]
|
null | null | 2404.06549 | null | null | http://arxiv.org/pdf/2404.06549v1 | 2024-04-09T18:02:01Z | 2024-04-09T18:02:01Z | Variational Stochastic Gradient Descent for Deep Neural Networks | Optimizing deep neural networks is one of the main tasks in successful deep learning. Current state-of-the-art optimizers are adaptive gradient-based optimization methods such as Adam. Recently, there has been an increasing interest in formulating gradient-based optimizers in a probabilistic framework for better estimation of gradients and modeling uncertainties. Here, we propose to combine both approaches, resulting in the Variational Stochastic Gradient Descent (VSGD) optimizer. We model gradient updates as a probabilistic model and utilize stochastic variational inference (SVI) to derive an efficient and effective update rule. Further, we show how our VSGD method relates to other adaptive gradient-based optimizers like Adam. Lastly, we carry out experiments on two image classification datasets and four deep neural network architectures, where we show that VSGD outperforms Adam and SGD. | [
"['Haotian Chen' 'Anna Kuzina' 'Babak Esmaeili' 'Jakub M Tomczak']"
]
|
null | null | 2404.06563 | null | null | http://arxiv.org/pdf/2404.06563v1 | 2024-04-09T18:27:59Z | 2024-04-09T18:27:59Z | Demonstration of MaskSearch: Efficiently Querying Image Masks for
Machine Learning Workflows | We demonstrate MaskSearch, a system designed to accelerate queries over databases of image masks generated by machine learning models. MaskSearch formalizes and accelerates a new category of queries for retrieving images and their corresponding masks based on mask properties, which support various applications, from identifying spurious correlations learned by models to exploring discrepancies between model saliency and human attention. This demonstration makes the following contributions:(1) the introduction of MaskSearch's graphical user interface (GUI), which enables interactive exploration of image databases through mask properties, (2) hands-on opportunities for users to explore MaskSearch's capabilities and constraints within machine learning workflows, and (3) an opportunity for conference attendees to understand how MaskSearch accelerates queries over image masks. | [
"['Lindsey Linxi Wei' 'Chung Yik Edward Yeung' 'Hongjian Yu'\n 'Jingchuan Zhou' 'Dong He' 'Magdalena Balazinska']"
]
|
null | null | 2404.06579 | null | null | http://arxiv.org/pdf/2404.06579v1 | 2024-04-09T19:02:12Z | 2024-04-09T19:02:12Z | Less is More for Improving Automatic Evaluation of Factual Consistency | Assessing the factual consistency of automatically generated texts in relation to source context is crucial for developing reliable natural language generation applications. Recent literature proposes AlignScore which uses a unified alignment model to evaluate factual consistency and substantially outperforms previous methods across many benchmark tasks. In this paper, we take a closer look of datasets used in AlignScore and uncover an unexpected finding: utilizing a smaller number of data points can actually improve performance. We process the original AlignScore training dataset to remove noise, augment with robustness-enhanced samples, and utilize a subset comprising 10% of the data to train an improved factual consistency evaluation model, we call LIM-RA (Less Is More for Robust AlignScore). LIM-RA demonstrates superior performance, consistently outperforming AlignScore and other strong baselines like ChatGPT across four benchmarks (two utilizing traditional natural language generation datasets and two focused on large language model outputs). Our experiments show that LIM-RA achieves the highest score on 24 of the 33 test datasets, while staying competitive on the rest, establishing the new state-of-the-art benchmarks. | [
"['Tong Wang' 'Ninad Kulkarni' 'Yanjun Qi']"
]
|
null | null | 2404.06583 | null | null | http://arxiv.org/pdf/2404.06583v1 | 2024-04-09T19:25:16Z | 2024-04-09T19:25:16Z | Lecture notes on rough paths and applications to machine learning | These notes expound the recent use of the signature transform and rough path theory in data science and machine learning. We develop the core theory of the signature from first principles and then survey some recent popular applications of this approach, including signature-based kernel methods and neural rough differential equations. The notes are based on a course given by the two authors at Imperial College London. | [
"['Thomas Cass' 'Cristopher Salvi']"
]
|
null | null | 2404.06589 | null | null | http://arxiv.org/pdf/2404.06589v2 | 2024-06-23T14:54:11Z | 2024-04-09T19:33:05Z | Leveraging Latents for Efficient Thermography Classification and
Segmentation | Breast cancer is a prominent health concern worldwide, currently being the secondmost common and second-deadliest type of cancer in women. While current breast cancer diagnosis mainly relies on mammography imaging, in recent years the use of thermography for breast cancer imaging has been garnering growing popularity. Thermographic imaging relies on infrared cameras to capture body-emitted heat distributions. While these heat signatures have proven useful for computer-vision systems for accurate breast cancer segmentation and classification, prior work often relies on handcrafted feature engineering or complex architectures, potentially limiting the comparability and applicability of these methods. In this work, we present a novel algorithm for both breast cancer classification and segmentation. Rather than focusing efforts on manual feature and architecture engineering, our algorithm focuses on leveraging an informative, learned feature space, thus making our solution simpler to use and extend to other frameworks and downstream tasks, as well as more applicable to data-scarce settings. Our classification produces SOTA results, while we are the first work to produce segmentation regions studied in this paper. | [
"['Tamir Shor' 'Chaim Baskin' 'Alex Bronstein']"
]
|
null | null | 2404.06593 | null | null | http://arxiv.org/pdf/2404.06593v1 | 2024-04-09T19:49:01Z | 2024-04-09T19:49:01Z | Spatially Optimized Compact Deep Metric Learning Model for Similarity
Search | Spatial optimization is often overlooked in many computer vision tasks. Filters should be able to recognize the features of an object regardless of where it is in the image. Similarity search is a crucial task where spatial features decide an important output. The capacity of convolution to capture visual patterns across various locations is limited. In contrast to convolution, the involution kernel is dynamically created at each pixel based on the pixel value and parameters that have been learned. This study demonstrates that utilizing a single layer of involution feature extractor alongside a compact convolution model significantly enhances the performance of similarity search. Additionally, we improve predictions by using the GELU activation function rather than the ReLU. The negligible amount of weight parameters in involution with a compact model with better performance makes the model very useful in real-world implementations. Our proposed model is below 1 megabyte in size. We have experimented with our proposed methodology and other models on CIFAR-10, FashionMNIST, and MNIST datasets. Our proposed method outperforms across all three datasets. | [
"['Md. Farhadul Islam' 'Md. Tanzim Reza' 'Meem Arafat Manab'\n 'Mohammad Rakibul Hasan Mahin' 'Sarah Zabeen' 'Jannatun Noor']"
]
|
null | null | 2404.06599 | null | null | http://arxiv.org/pdf/2404.06599v1 | 2024-04-09T20:06:25Z | 2024-04-09T20:06:25Z | FMDA-OT: Federated Multi-source Domain Adaptation Through Optimal
Transport | Multi-source Domain Adaptation (MDA) aims to adapt models trained on multiple labeled source domains to an unlabeled target domain. In this paper, we introduce our approach as a collaborative MDA framework, which comprises two adaptation phases. Firstly, we conduct domain adaptation for each source individually with the target, utilizing optimal transport. Then, in the second phase, which constitutes the final part of the framework, we design the architecture of centralized federated learning to collaborate the N models representing the N sources. This architecture offers the advantage of using the sources without accessing their data, thus resolving data privacy issues inherent in domain adaptation. Additionally, during this phase, the server guides and fine-tunes the adaptation using a small number of pseudo-labeled samples available in the target domain, referred to as the target validation subset of the dataset. | [
"['Omar Ghannou' 'Younès Bennani']"
]
|
null | null | 2404.06619 | null | null | http://arxiv.org/pdf/2404.06619v1 | 2024-04-09T21:09:22Z | 2024-04-09T21:09:22Z | FairPair: A Robust Evaluation of Biases in Language Models through
Paired Perturbations | The accurate evaluation of differential treatment in language models to specific groups is critical to ensuring a positive and safe user experience. An ideal evaluation should have the properties of being robust, extendable to new groups or attributes, and being able to capture biases that appear in typical usage (rather than just extreme, rare cases). Relatedly, bias evaluation should surface not only egregious biases but also ones that are subtle and commonplace, such as a likelihood for talking about appearances with regard to women. We present FairPair, an evaluation framework for assessing differential treatment that occurs during ordinary usage. FairPair operates through counterfactual pairs, but crucially, the paired continuations are grounded in the same demographic group, which ensures equivalent comparison. Additionally, unlike prior work, our method factors in the inherent variability that comes from the generation process itself by measuring the sampling variability. We present an evaluation of several commonly used generative models and a qualitative analysis that indicates a preference for discussing family and hobbies with regard to women. | [
"['Jane Dwivedi-Yu' 'Raaz Dwivedi' 'Timo Schick']"
]
|
null | null | 2404.06641 | null | null | http://arxiv.org/pdf/2404.06641v1 | 2024-04-09T22:31:10Z | 2024-04-09T22:31:10Z | Federated learning model for predicting major postoperative
complications | Background: The accurate prediction of postoperative complication risk using Electronic Health Records (EHR) and artificial intelligence shows great potential. Training a robust artificial intelligence model typically requires large-scale and diverse datasets. In reality, collecting medical data often encounters challenges surrounding privacy protection. Methods: This retrospective cohort study includes adult patients who were admitted to UFH Gainesville (GNV) (n = 79,850) and Jacksonville (JAX) (n = 28,636) for any type of inpatient surgical procedure. Using perioperative and intraoperative features, we developed federated learning models to predict nine major postoperative complications (i.e., prolonged intensive care unit stay and mechanical ventilation). We compared federated learning models with local learning models trained on a single site and central learning models trained on pooled dataset from two centers. Results: Our federated learning models achieved the area under the receiver operating characteristics curve (AUROC) values ranged from 0.81 for wound complications to 0.92 for prolonged ICU stay at UFH GNV center. At UFH JAX center, these values ranged from 0.73-0.74 for wound complications to 0.92-0.93 for hospital mortality. Federated learning models achieved comparable AUROC performance to central learning models, except for prolonged ICU stay, where the performance of federated learning models was slightly higher than central learning models at UFH GNV center, but slightly lower at UFH JAX center. In addition, our federated learning model obtained comparable performance to the best local learning model at each center, demonstrating strong generalizability. Conclusion: Federated learning is shown to be a useful tool to train robust and generalizable models from large scale data across multiple institutions where data protection barriers are high. | [
"['Yonggi Park' 'Yuanfang Ren' 'Benjamin Shickel' 'Ziyuan Guan'\n 'Ayush Patela' 'Yingbo Ma' 'Zhenhong Hu' 'Tyler J. Loftus'\n 'Parisa Rashidi' 'Tezcan Ozrazgat-Baslanti' 'Azra Bihorac']"
]
|
null | null | 2404.06647 | null | null | http://arxiv.org/pdf/2404.06647v2 | 2024-04-11T02:09:23Z | 2024-04-09T22:55:06Z | From Protoscience to Epistemic Monoculture: How Benchmarking Set the
Stage for the Deep Learning Revolution | Over the past decade, AI research has focused heavily on building ever-larger deep learning models. This approach has simultaneously unlocked incredible achievements in science and technology, and hindered AI from overcoming long-standing limitations with respect to explainability, ethical harms, and environmental efficiency. Drawing on qualitative interviews and computational analyses, our three-part history of AI research traces the creation of this "epistemic monoculture" back to a radical reconceptualization of scientific progress that began in the late 1980s. In the first era of AI research (1950s-late 1980s), researchers and patrons approached AI as a "basic" science that would advance through autonomous exploration and organic assessments of progress (e.g., peer-review, theoretical consensus). The failure of this approach led to a retrenchment of funding in the 1980s. Amid this "AI Winter," an intervention by the U.S. government reoriented the field towards measurable progress on tasks of military and commercial interest. A new evaluation system called "benchmarking" provided an objective way to quantify progress on tasks by focusing exclusively on increasing predictive accuracy on example datasets. Distilling science down to verifiable metrics clarified the roles of scientists, allowed the field to rapidly integrate talent, and provided clear signals of significance and progress. But history has also revealed a tradeoff to this streamlined approach to science: the consolidation around external interests and inherent conservatism of benchmarking has disincentivized exploration beyond scaling monoculture. In the discussion, we explain how AI's monoculture offers a compelling challenge to the belief that basic, exploration-driven research is needed for scientific progress. Implications for the spread of AI monoculture to other sciences in the era of generative AI are also discussed. | [
"['Bernard J. Koch' 'David Peterson']"
]
|
null | null | 2404.06668 | null | null | http://arxiv.org/pdf/2404.06668v1 | 2024-04-10T00:52:54Z | 2024-04-10T00:52:54Z | Forecasting the Future with Future Technologies: Advancements in Large
Meteorological Models | The field of meteorological forecasting has undergone a significant transformation with the integration of large models, especially those employing deep learning techniques. This paper reviews the advancements and applications of these models in weather prediction, emphasizing their role in transforming traditional forecasting methods. Models like FourCastNet, Pangu-Weather, GraphCast, ClimaX, and FengWu have made notable contributions by providing accurate, high-resolution forecasts, surpassing the capabilities of traditional Numerical Weather Prediction (NWP) models. These models utilize advanced neural network architectures, such as Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Transformers, to process diverse meteorological data, enhancing predictive accuracy across various time scales and spatial resolutions. The paper addresses challenges in this domain, including data acquisition and computational demands, and explores future opportunities for model optimization and hardware advancements. It underscores the integration of artificial intelligence with conventional meteorological techniques, promising improved weather prediction accuracy and a significant contribution to addressing climate-related challenges. This synergy positions large models as pivotal in the evolving landscape of meteorological forecasting. | [
"['Hailong Shu' 'Yue Wang' 'Weiwei Song' 'Huichuang Guo' 'Zhen Song']"
]
|
null | null | 2404.06675 | null | null | http://arxiv.org/pdf/2404.06675v1 | 2024-04-10T01:35:17Z | 2024-04-10T01:35:17Z | Toward Cross-Layer Energy Optimizations in Machine Learning Systems | The enormous energy consumption of machine learning (ML) and generative AI workloads shows no sign of waning, taking a toll on operating costs, power delivery, and environmental sustainability. Despite a long line of research on energy-efficient hardware, we found that software plays a critical role in ML energy optimization through two recent works: Zeus and Perseus. This is especially true for large language models (LLMs) because their model sizes and, therefore, energy demands are growing faster than hardware efficiency improvements. Therefore, we advocate for a cross-layer approach for energy optimizations in ML systems, where hardware provides architectural support that pushes energy-efficient software further, while software leverages and abstracts the hardware to develop techniques that bring hardware-agnostic energy-efficiency gains. | [
"['Jae-Won Chung' 'Mosharaf Chowdhury']"
]
|
null | null | 2404.06676 | null | null | http://arxiv.org/pdf/2404.06676v1 | 2024-04-10T01:37:41Z | 2024-04-10T01:37:41Z | Topological Feature Search Method for Multichannel EEG: Application in
ADHD classification | In recent years, the preliminary diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) using electroencephalography (EEG) has garnered attention from researchers. EEG, known for its expediency and efficiency, plays a pivotal role in the diagnosis and treatment of ADHD. However, the non-stationarity of EEG signals and inter-subject variability pose challenges to the diagnostic and classification processes. Topological Data Analysis (TDA) offers a novel perspective for ADHD classification, diverging from traditional time-frequency domain features. Yet, conventional TDA models are restricted to single-channel time series and are susceptible to noise, leading to the loss of topological features in persistence diagrams.This paper presents an enhanced TDA approach applicable to multi-channel EEG in ADHD. Initially, optimal input parameters for multi-channel EEG are determined. Subsequently, each channel's EEG undergoes phase space reconstruction (PSR) followed by the utilization of k-Power Distance to Measure (k-PDTM) for approximating ideal point clouds. Then, multi-dimensional time series are re-embedded, and TDA is applied to obtain topological feature information. Gaussian function-based Multivariate Kernel Density Estimation (MKDE) is employed in the merger persistence diagram to filter out desired topological feature mappings. Finally, persistence image (PI) method is utilized to extract topological features, and the influence of various weighting functions on the results is discussed.The effectiveness of our method is evaluated using the IEEE ADHD dataset. Results demonstrate that the accuracy, sensitivity, and specificity reach 85.60%, 83.61%, and 88.33%, respectively. Compared to traditional TDA methods, our method was effectively improved and outperforms typical nonlinear descriptors. These findings indicate that our method exhibits higher precision and robustness. | [
"['Tianming Cai' 'Guoying Zhao' 'Junbin Zang' 'Chen Zong' 'Zhidong Zhang'\n 'Chenyang Xue']"
]
|
null | null | 2404.06681 | null | null | http://arxiv.org/pdf/2404.06681v1 | 2024-04-10T02:02:34Z | 2024-04-10T02:02:34Z | Causal Unit Selection using Tractable Arithmetic Circuits | The unit selection problem aims to find objects, called units, that optimize a causal objective function which describes the objects' behavior in a causal context (e.g., selecting customers who are about to churn but would most likely change their mind if encouraged). While early studies focused mainly on bounding a specific class of counterfactual objective functions using data, more recent work allows one to find optimal units exactly by reducing the causal objective to a classical objective on a meta-model, and then applying a variant of the classical Variable Elimination (VE) algorithm to the meta-model -- assuming a fully specified causal model is available. In practice, however, finding optimal units using this approach can be very expensive because the used VE algorithm must be exponential in the constrained treewidth of the meta-model, which is larger and denser than the original model. We address this computational challenge by introducing a new approach for unit selection that is not necessarily limited by the constrained treewidth. This is done through compiling the meta-model into a special class of tractable arithmetic circuits that allows the computation of optimal units in time linear in the circuit size. We finally present empirical results on random causal models that show order-of-magnitude speedups based on the proposed method for solving unit selection. | [
"['Haiying Huang' 'Adnan Darwiche']"
]
|
null | null | 2404.06690 | null | null | http://arxiv.org/pdf/2404.06690v2 | 2024-05-29T07:30:20Z | 2024-04-10T02:32:58Z | CoVoMix: Advancing Zero-Shot Speech Generation for Human-like
Multi-talker Conversations | Recent advancements in zero-shot text-to-speech (TTS) modeling have led to significant strides in generating high-fidelity and diverse speech. However, dialogue generation, along with achieving human-like naturalness in speech, continues to be a challenge. In this paper, we introduce CoVoMix: Conversational Voice Mixture Generation, a novel model for zero-shot, human-like, multi-speaker, multi-round dialogue speech generation. CoVoMix first converts dialogue text into multiple streams of discrete tokens, with each token stream representing semantic information for individual talkers. These token streams are then fed into a flow-matching based acoustic model to generate mixed mel-spectrograms. Finally, the speech waveforms are produced using a HiFi-GAN model. Furthermore, we devise a comprehensive set of metrics for measuring the effectiveness of dialogue modeling and generation. Our experimental results show that CoVoMix can generate dialogues that are not only human-like in their naturalness and coherence but also involve multiple talkers engaging in multiple rounds of conversation. This is exemplified by instances generated in a single channel where one speaker's utterance is seamlessly mixed with another's interjections or laughter, indicating the latter's role as an attentive listener. Audio samples are available at https://aka.ms/covomix. | [
"['Leying Zhang' 'Yao Qian' 'Long Zhou' 'Shujie Liu' 'Dongmei Wang'\n 'Xiaofei Wang' 'Midia Yousefi' 'Yanmin Qian' 'Jinyu Li' 'Lei He'\n 'Sheng Zhao' 'Michael Zeng']"
]
|
null | null | 2404.06691 | null | null | http://arxiv.org/pdf/2404.06691v1 | 2024-04-10T02:37:24Z | 2024-04-10T02:37:24Z | Latent Chemical Space Searching for Plug-in Multi-objective Molecule
Generation | Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology. However, challenges remain in multi-objective generation, model adaptability, and practical application in drug discovery. In this study, we developed a versatile 'plug-in' molecular generation model that incorporates multiple objectives related to target affinity, drug-likeness, and synthesizability, facilitating its application in various drug development contexts. We improved the Particle Swarm Optimization (PSO) in the context of drug discoveries, and identified PSO-ENP as the optimal variant for multi-objective molecular generation and optimization through comparative experiments. The model also incorporates a novel target-ligand affinity predictor, enhancing the model's utility by supporting three-dimensional information and improving synthetic feasibility. Case studies focused on generating and optimizing drug-like big marine natural products were performed, underscoring PSO-ENP's effectiveness and demonstrating its considerable potential for practical drug discovery applications. | [
"['Ningfeng Liu' 'Jie Yu' 'Siyu Xiu' 'Xinfang Zhao' 'Siyu Lin' 'Bo Qiang'\n 'Ruqiu Zheng' 'Hongwei Jin' 'Liangren Zhang' 'Zhenming Liu']"
]
|
null | null | 2404.06694 | null | null | http://arxiv.org/pdf/2404.06694v2 | 2024-04-22T21:27:36Z | 2024-04-10T02:54:18Z | How to Craft Backdoors with Unlabeled Data Alone? | Relying only on unlabeled data, Self-supervised learning (SSL) can learn rich features in an economical and scalable way. As the drive-horse for building foundation models, SSL has received a lot of attention recently with wide applications, which also raises security concerns where backdoor attack is a major type of threat: if the released dataset is maliciously poisoned, backdoored SSL models can behave badly when triggers are injected to test samples. The goal of this work is to investigate this potential risk. We notice that existing backdoors all require a considerable amount of emph{labeled} data that may not be available for SSL. To circumvent this limitation, we explore a more restrictive setting called no-label backdoors, where we only have access to the unlabeled data alone, where the key challenge is how to select the proper poison set without using label information. We propose two strategies for poison selection: clustering-based selection using pseudolabels, and contrastive selection derived from the mutual information principle. Experiments on CIFAR-10 and ImageNet-100 show that both no-label backdoors are effective on many SSL methods and outperform random poisoning by a large margin. Code will be available at https://github.com/PKU-ML/nlb. | [
"['Yifei Wang' 'Wenhan Ma' 'Stefanie Jegelka' 'Yisen Wang']"
]
|
null | null | 2404.06720 | null | null | http://arxiv.org/pdf/2404.06720v1 | 2024-04-10T04:15:50Z | 2024-04-10T04:15:50Z | Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory
Tradeoff for Feasibility Problems | In this paper we provide oracle complexity lower bounds for finding a point in a given set using a memory-constrained algorithm that has access to a separation oracle. We assume that the set is contained within the unit $d$-dimensional ball and contains a ball of known radius $epsilon>0$. This setup is commonly referred to as the feasibility problem. We show that to solve feasibility problems with accuracy $epsilon geq e^{-d^{o(1)}}$, any deterministic algorithm either uses $d^{1+delta}$ bits of memory or must make at least $1/(d^{0.01delta }epsilon^{2frac{1-delta}{1+1.01 delta}-o(1)})$ oracle queries, for any $deltain[0,1]$. Additionally, we show that randomized algorithms either use $d^{1+delta}$ memory or make at least $1/(d^{2delta} epsilon^{2(1-4delta)-o(1)})$ queries for any $deltain[0,frac{1}{4}]$. Because gradient descent only uses linear memory $mathcal O(dln 1/epsilon)$ but makes $Omega(1/epsilon^2)$ queries, our results imply that it is Pareto-optimal in the oracle complexity/memory tradeoff. Further, our results show that the oracle complexity for deterministic algorithms is always polynomial in $1/epsilon$ if the algorithm has less than quadratic memory in $d$. This reveals a sharp phase transition since with quadratic $mathcal O(d^2 ln1/epsilon)$ memory, cutting plane methods only require $mathcal O(dln 1/epsilon)$ queries. | [
"['Moise Blanchard']"
]
|
null | null | 2404.06723 | null | null | http://arxiv.org/pdf/2404.06723v1 | 2024-04-10T04:19:59Z | 2024-04-10T04:19:59Z | Global Contrastive Training for Multimodal Electronic Health Records
with Language Supervision | Modern electronic health records (EHRs) hold immense promise in tracking personalized patient health trajectories through sequential deep learning, owing to their extensive breadth, scale, and temporal granularity. Nonetheless, how to effectively leverage multiple modalities from EHRs poses significant challenges, given its complex characteristics such as high dimensionality, multimodality, sparsity, varied recording frequencies, and temporal irregularities. To this end, this paper introduces a novel multimodal contrastive learning framework, specifically focusing on medical time series and clinical notes. To tackle the challenge of sparsity and irregular time intervals in medical time series, the framework integrates temporal cross-attention transformers with a dynamic embedding and tokenization scheme for learning multimodal feature representations. To harness the interconnected relationships between medical time series and clinical notes, the framework equips a global contrastive loss, aligning a patient's multimodal feature representations with the corresponding discharge summaries. Since discharge summaries uniquely pertain to individual patients and represent a holistic view of the patient's hospital stay, machine learning models are led to learn discriminative multimodal features via global contrasting. Extensive experiments with a real-world EHR dataset demonstrated that our framework outperformed state-of-the-art approaches on the exemplar task of predicting the occurrence of nine postoperative complications for more than 120,000 major inpatient surgeries using multimodal data from UF health system split among three hospitals (UF Health Gainesville, UF Health Jacksonville, and UF Health Jacksonville-North). | [
"['Yingbo Ma' 'Suraj Kolla' 'Zhenhong Hu' 'Dhruv Kaliraman'\n 'Victoria Nolan' 'Ziyuan Guan' 'Yuanfang Ren' 'Brooke Armfield'\n 'Tezcan Ozrazgat-Baslanti' 'Jeremy A. Balch' 'Tyler J. Loftus'\n 'Parisa Rashidi' 'Azra Bihorac' 'Benjamin Shickel']"
]
|
null | null | 2404.06732 | null | null | http://arxiv.org/abs/2404.06732v1 | 2024-04-10T04:36:24Z | 2024-04-10T04:36:24Z | Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient
Control of Autonomous and Human-Driven Vehicles | With the increasing presence of autonomous vehicles (AVs) on public roads, developing robust control strategies to navigate the uncertainty of human-driven vehicles (HVs) is crucial. This paper introduces an advanced method for modeling HV behavior, combining a first-principles model with Gaussian process (GP) learning to enhance velocity prediction accuracy and provide a measurable uncertainty. We validated this innovative HV model using real-world data from field experiments and applied it to develop a GP-enhanced model predictive control (GP-MPC) strategy. This strategy aims to improve safety in mixed vehicle platoons by integrating uncertainty assessment into distance constraints. Comparative simulation studies with a conventional model predictive control (MPC) approach demonstrated that our GP-MPC strategy ensures more reliable safe distancing and fosters efficient vehicular dynamics, achieving notably higher speeds within the platoon. By incorporating a sparse GP technique in HV modeling and adopting a dynamic GP prediction within the MPC framework, we significantly reduced the computation time of GP-MPC, marking it only 4.6% higher than that of the conventional MPC. This represents a substantial improvement, making the process about 100 times faster than our preliminary work without these approximations. Our findings underscore the effectiveness of learning-based HV modeling in enhancing both safety and operational efficiency in mixed-traffic environments, paving the way for more harmonious AV-HV interactions. | [
"['Jie Wang' 'Yash Vardhan Pant' 'Lei Zhao' 'Michał Antkiewicz'\n 'Krzysztof Czarnecki']"
]
|
null | null | 2404.06735 | null | null | http://arxiv.org/pdf/2404.06735v1 | 2024-04-10T04:49:00Z | 2024-04-10T04:49:00Z | A Copula Graphical Model for Multi-Attribute Data using Optimal
Transport | Motivated by modern data forms such as images and multi-view data, the multi-attribute graphical model aims to explore the conditional independence structure among vectors. Under the Gaussian assumption, the conditional independence between vectors is characterized by blockwise zeros in the precision matrix. To relax the restrictive Gaussian assumption, in this paper, we introduce a novel semiparametric multi-attribute graphical model based on a new copula named Cyclically Monotone Copula. This new copula treats the distribution of the node vectors as multivariate marginals and transforms them into Gaussian distributions based on the optimal transport theory. Since the model allows the node vectors to have arbitrary continuous distributions, it is more flexible than the classical Gaussian copula method that performs coordinatewise Gaussianization. We establish the concentration inequalities of the estimated covariance matrices and provide sufficient conditions for selection consistency of the group graphical lasso estimator. For the setting with high-dimensional attributes, a {Projected Cyclically Monotone Copula} model is proposed to address the curse of dimensionality issue that arises from solving high-dimensional optimal transport problems. Numerical results based on synthetic and real data show the efficiency and flexibility of our methods. | [
"['Qi Zhang' 'Bing Li' 'Lingzhou Xue']"
]
|
null | null | 2404.06737 | null | null | http://arxiv.org/pdf/2404.06737v4 | 2024-06-04T00:35:06Z | 2024-04-10T04:55:57Z | Disguised Copyright Infringement of Latent Diffusion Models | Copyright infringement may occur when a generative model produces samples substantially similar to some copyrighted data that it had access to during the training phase. The notion of access usually refers to including copyrighted samples directly in the training dataset, which one may inspect to identify an infringement. We argue that such visual auditing largely overlooks a concealed copyright infringement, where one constructs a disguise that looks drastically different from the copyrighted sample yet still induces the effect of training Latent Diffusion Models on it. Such disguises only require indirect access to the copyrighted material and cannot be visually distinguished, thus easily circumventing the current auditing tools. In this paper, we provide a better understanding of such disguised copyright infringement by uncovering the disguises generation algorithm, the revelation of the disguises, and importantly, how to detect them to augment the existing toolbox. Additionally, we introduce a broader notion of acknowledgment for comprehending such indirect access. Our code is available at https://github.com/watml/disguised_copyright_infringement. | [
"['Yiwei Lu' 'Matthew Y. R. Yang' 'Zuoqiu Liu' 'Gautam Kamath'\n 'Yaoliang Yu']"
]
|
null | null | 2404.06749 | null | null | http://arxiv.org/pdf/2404.06749v1 | 2024-04-10T05:32:03Z | 2024-04-10T05:32:03Z | CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for
Modeling Complex Systems and Data Assimilation | A new knowledge-based and machine learning hybrid modeling approach, called conditional Gaussian neural stochastic differential equation (CGNSDE), is developed to facilitate modeling complex dynamical systems and implementing analytic formulae of the associated data assimilation (DA). In contrast to the standard neural network predictive models, the CGNSDE is designed to effectively tackle both forward prediction tasks and inverse state estimation problems. The CGNSDE starts by exploiting a systematic causal inference via information theory to build a simple knowledge-based nonlinear model that nevertheless captures as much explainable physics as possible. Then, neural networks are supplemented to the knowledge-based model in a specific way, which not only characterizes the remaining features that are challenging to model with simple forms but also advances the use of analytic formulae to efficiently compute the nonlinear DA solution. These analytic formulae are used as an additional computationally affordable loss to train the neural networks that directly improve the DA accuracy. This DA loss function promotes the CGNSDE to capture the interactions between state variables and thus advances its modeling skills. With the DA loss, the CGNSDE is more capable of estimating extreme events and quantifying the associated uncertainty. Furthermore, crucial physical properties in many complex systems, such as the translate-invariant local dependence of state variables, can significantly simplify the neural network structures and facilitate the CGNSDE to be applied to high-dimensional systems. Numerical experiments based on chaotic systems with intermittency and strong non-Gaussian features indicate that the CGNSDE outperforms knowledge-based regression models, and the DA loss further enhances the modeling skills of the CGNSDE. | [
"['Chuanqi Chen' 'Nan Chen' 'Jin-Long Wu']"
]
|
null | null | 2404.06756 | null | null | http://arxiv.org/pdf/2404.06756v1 | 2024-04-10T05:44:28Z | 2024-04-10T05:44:28Z | CrimeAlarm: Towards Intensive Intent Dynamics in Fine-grained Crime
Prediction | Granularity and accuracy are two crucial factors for crime event prediction. Within fine-grained event classification, multiple criminal intents may alternately exhibit in preceding sequential events, and progress differently in next. Such intensive intent dynamics makes training models hard to capture unobserved intents, and thus leads to sub-optimal generalization performance, especially in the intertwining of numerous potential events. To capture comprehensive criminal intents, this paper proposes a fine-grained sequential crime prediction framework, CrimeAlarm, that equips with a novel mutual distillation strategy inspired by curriculum learning. During the early training phase, spot-shared criminal intents are captured through high-confidence sequence samples. In the later phase, spot-specific intents are gradually learned by increasing the contribution of low-confidence sequences. Meanwhile, the output probability distributions are reciprocally learned between prediction networks to model unobserved criminal intents. Extensive experiments show that CrimeAlarm outperforms state-of-the-art methods in terms of NDCG@5, with improvements of 4.51% for the NYC16 and 7.73% for the CHI18 in accuracy measures. | [
"['Kaixi Hu' 'Lin Li' 'Qing Xie' 'Xiaohui Tao' 'Guandong Xu']"
]
|
null | null | 2404.06757 | null | null | http://arxiv.org/pdf/2404.06757v1 | 2024-04-10T05:53:25Z | 2024-04-10T05:53:25Z | Language Generation in the Limit | Although current large language models are complex, the most basic specifications of the underlying language generation problem itself are simple to state: given a finite set of training samples from an unknown language, produce valid new strings from the language that don't already appear in the training data. Here we ask what we can conclude about language generation using only this specification, without further assumptions. In particular, suppose that an adversary enumerates the strings of an unknown target language L that is known only to come from one of a possibly infinite list of candidates. A computational agent is trying to learn to generate from this language; we say that the agent generates from L in the limit if after some finite point in the enumeration of L, the agent is able to produce new elements that come exclusively from L and that have not yet been presented by the adversary. Our main result is that there is an agent that is able to generate in the limit for every countable list of candidate languages. This contrasts dramatically with negative results due to Gold and Angluin in a well-studied model of language learning where the goal is to identify an unknown language from samples; the difference between these results suggests that identifying a language is a fundamentally different problem than generating from it. | [
"['Jon Kleinberg' 'Sendhil Mullainathan']"
]
|
null | null | 2404.06776 | null | null | http://arxiv.org/pdf/2404.06776v1 | 2024-04-10T06:35:25Z | 2024-04-10T06:35:25Z | Logit Calibration and Feature Contrast for Robust Federated Learning on
Non-IID Data | Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training on devices in edge networks. However, challenges arise due to vulnerability to adversarial examples (AEs) and the non-independent and identically distributed (non-IID) nature of data distribution among devices, hindering the deployment of adversarially robust and accurate learning models at the edge. While adversarial training (AT) is commonly acknowledged as an effective defense strategy against adversarial attacks in centralized training, we shed light on the adverse effects of directly applying AT in FL that can severely compromise accuracy, especially in non-IID challenges. Given this limitation, this paper proposes FatCC, which incorporates local logit underline{C}alibration and global feature underline{C}ontrast into the vanilla federated adversarial training (underline{FAT}) process from both logit and feature perspectives. This approach can effectively enhance the federated system's robust accuracy (RA) and clean accuracy (CA). First, we propose logit calibration, where the logits are calibrated during local adversarial updates, thereby improving adversarial robustness. Second, FatCC introduces feature contrast, which involves a global alignment term that aligns each local representation with unbiased global features, thus further enhancing robustness and accuracy in federated adversarial environments. Extensive experiments across multiple datasets demonstrate that FatCC achieves comparable or superior performance gains in both CA and RA compared to other baselines. | [
"['Yu Qiao' 'Chaoning Zhang' 'Apurba Adhikary' 'Choong Seon Hong']"
]
|
null | null | 2404.06787 | null | null | http://arxiv.org/pdf/2404.06787v1 | 2024-04-10T06:58:58Z | 2024-04-10T06:58:58Z | Private Wasserstein Distance with Random Noises | Wasserstein distance is a principle measure of data divergence from a distributional standpoint. However, its application becomes challenging in the context of data privacy, where sharing raw data is restricted. Prior attempts have employed techniques like Differential Privacy or Federated optimization to approximate Wasserstein distance. Nevertheless, these approaches often lack accuracy and robustness against potential attack. In this study, we investigate the underlying triangular properties within the Wasserstein space, leading to a straightforward solution named TriangleWad. This approach enables the computation of Wasserstein distance between datasets stored across different entities. Notably, TriangleWad is 20 times faster, making raw data information truly invisible, enhancing resilience against attacks, and without sacrificing estimation accuracy. Through comprehensive experimentation across various tasks involving both image and text data, we demonstrate its superior performance and generalizations. | [
"['Wenqian Li' 'Haozhi Wang' 'Zhe Huang' 'Yan Pang']"
]
|
null | null | 2404.06795 | null | null | http://arxiv.org/pdf/2404.06795v1 | 2024-04-10T07:34:37Z | 2024-04-10T07:34:37Z | Extracting Clean and Balanced Subset for Noisy Long-tailed
Classification | Real-world datasets usually are class-imbalanced and corrupted by label noise. To solve the joint issue of long-tailed distribution and label noise, most previous works usually aim to design a noise detector to distinguish the noisy and clean samples. Despite their effectiveness, they may be limited in handling the joint issue effectively in a unified way. In this work, we develop a novel pseudo labeling method using class prototypes from the perspective of distribution matching, which can be solved with optimal transport (OT). By setting a manually-specific probability measure and using a learned transport plan to pseudo-label the training samples, the proposed method can reduce the side-effects of noisy and long-tailed data simultaneously. Then we introduce a simple yet effective filter criteria by combining the observed labels and pseudo labels to obtain a more balanced and less noisy subset for a robust model training. Extensive experiments demonstrate that our method can extract this class-balanced subset with clean labels, which brings effective performance gains for long-tailed classification with label noise. | [
"['Zhuo Li' 'He Zhao' 'Zhen Li' 'Tongliang Liu' 'Dandan Guo' 'Xiang Wan']"
]
|
null | null | 2404.06808 | null | null | http://arxiv.org/pdf/2404.06808v1 | 2024-04-10T07:55:10Z | 2024-04-10T07:55:10Z | Formation-Controlled Dimensionality Reduction | Dimensionality reduction represents the process of generating a low dimensional representation of high dimensional data. Motivated by the formation control of mobile agents, we propose a nonlinear dynamical system for dimensionality reduction. The system consists of two parts; the control of neighbor points, addressing local structures, and the control of remote points, accounting for global structures. We also include a brief mathematical observation of the model and its numerical procedure. Numerical experiments are performed on both synthetic and real datasets and comparisons with existing models demonstrate the soundness and effectiveness of the proposed model. | [
"['Taeuk Jeong' 'Yoon Mo Jung']"
]
|
null | null | 2404.06818 | null | null | http://arxiv.org/pdf/2404.06818v1 | 2024-04-10T08:06:15Z | 2024-04-10T08:06:15Z | Towards Efficient and Real-Time Piano Transcription Using Neural
Autoregressive Models | In recent years, advancements in neural network designs and the availability of large-scale labeled datasets have led to significant improvements in the accuracy of piano transcription models. However, most previous work focused on high-performance offline transcription, neglecting deliberate consideration of model size. The goal of this work is to implement real-time inference for piano transcription while ensuring both high performance and lightweight. To this end, we propose novel architectures for convolutional recurrent neural networks, redesigning an existing autoregressive piano transcription model. First, we extend the acoustic module by adding a frequency-conditioned FiLM layer to the CNN module to adapt the convolutional filters on the frequency axis. Second, we improve note-state sequence modeling by using a pitchwise LSTM that focuses on note-state transitions within a note. In addition, we augment the autoregressive connection with an enhanced recursive context. Using these components, we propose two types of models; one for high performance and the other for high compactness. Through extensive experiments, we show that the proposed models are comparable to state-of-the-art models in terms of note accuracy on the MAESTRO dataset. We also investigate the effective model size and real-time inference latency by gradually streamlining the architecture. Finally, we conduct cross-data evaluation on unseen piano datasets and in-depth analysis to elucidate the effect of the proposed components in the view of note length and pitch range. | [
"['Taegyun Kwon' 'Dasaem Jeong' 'Juhan Nam']"
]
|
null | null | 2404.06824 | null | null | http://arxiv.org/pdf/2404.06824v1 | 2024-04-10T08:23:05Z | 2024-04-10T08:23:05Z | Error Mitigation for TDoA UWB Indoor Localization using Unsupervised
Machine Learning | Indoor positioning systems based on Ultra-wideband (UWB) technology are gaining recognition for their ability to provide cm-level localization accuracy. However, these systems often encounter challenges caused by dense multi-path fading, leading to positioning errors. To address this issue, in this letter, we propose a novel methodology for unsupervised anchor node selection using deep embedded clustering (DEC). Our approach uses an Auto Encoder (AE) before clustering, thereby better separating UWB features into separable clusters of UWB input signals. We furthermore investigate how to rank these clusters based on their cluster quality, allowing us to remove untrustworthy signals. Experimental results show the efficiency of our proposed method, demonstrating a significant 23.1% reduction in mean absolute error (MAE) compared to without anchor exclusion. Especially in the dense multi-path area, our algorithm achieves even more significant enhancements, reducing the MAE by 26.6% and the 95th percentile error by 49.3% compared to without anchor exclusion. | [
"['Phuong Bich Duong' 'Ben Van Herbruggen' 'Arne Broering' 'Adnan Shahid'\n 'Eli De Poorter']"
]
|
null | null | 2404.06831 | null | null | http://arxiv.org/pdf/2404.06831v3 | 2024-06-14T08:11:11Z | 2024-04-10T08:47:57Z | Generalized Linear Bandits with Limited Adaptivity | We study the generalized linear contextual bandit problem within the constraints of limited adaptivity. In this paper, we present two algorithms, $texttt{B-GLinCB}$ and $texttt{RS-GLinCB}$, that address, respectively, two prevalent limited adaptivity settings. Given a budget $M$ on the number of policy updates, in the first setting, the algorithm needs to decide upfront $M$ rounds at which it will update its policy, while in the second setting it can adaptively perform $M$ policy updates during its course. For the first setting, we design an algorithm $texttt{B-GLinCB}$, that incurs $tilde{O}(sqrt{T})$ regret when $M = Omegaleft( log{log T} right)$ and the arm feature vectors are generated stochastically. For the second setting, we design an algorithm $texttt{RS-GLinCB}$ that updates its policy $tilde{O}(log^2 T)$ times and achieves a regret of $tilde{O}(sqrt{T})$ even when the arm feature vectors are adversarially generated. Notably, in these bounds, we manage to eliminate the dependence on a key instance dependent parameter $kappa$, that captures non-linearity of the underlying reward model. Our novel approach for removing this dependence for generalized linear contextual bandits might be of independent interest. | [
"['Ayush Sawarni' 'Nirjhar Das' 'Siddharth Barman' 'Gaurav Sinha']"
]
|
null | null | 2404.06832 | null | null | http://arxiv.org/pdf/2404.06832v1 | 2024-04-10T08:48:09Z | 2024-04-10T08:48:09Z | SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection | Detecting anomalies in images has become a well-explored problem in both academia and industry. State-of-the-art algorithms are able to detect defects in increasingly difficult settings and data modalities. However, most current methods are not suited to address 3D objects captured from differing poses. While solutions using Neural Radiance Fields (NeRFs) have been proposed, they suffer from excessive computation requirements, which hinder real-world usability. For this reason, we propose the novel 3D Gaussian splatting-based framework SplatPose which, given multi-view images of a 3D object, accurately estimates the pose of unseen views in a differentiable manner, and detects anomalies in them. We achieve state-of-the-art results in both training and inference speed, and detection performance, even when using less training data than competing methods. We thoroughly evaluate our framework using the recently proposed Pose-agnostic Anomaly Detection benchmark and its multi-pose anomaly detection (MAD) data set. | [
"['Mathis Kruse' 'Marco Rudolph' 'Dominik Woiwode' 'Bodo Rosenhahn']"
]
|
null | null | 2404.06834 | null | null | http://arxiv.org/pdf/2404.06834v2 | 2024-04-12T13:47:07Z | 2024-04-10T08:52:12Z | Solving Parametric PDEs with Radial Basis Functions and Deep Neural
Networks | We propose the POD-DNN, a novel algorithm leveraging deep neural networks (DNNs) along with radial basis functions (RBFs) in the context of the proper orthogonal decomposition (POD) reduced basis method (RBM), aimed at approximating the parametric mapping of parametric partial differential equations on irregular domains. The POD-DNN algorithm capitalizes on the low-dimensional characteristics of the solution manifold for parametric equations, alongside the inherent offline-online computational strategy of RBM and DNNs. In numerical experiments, POD-DNN demonstrates significantly accelerated computation speeds during the online phase. Compared to other algorithms that utilize RBF without integrating DNNs, POD-DNN substantially improves the computational speed in the online inference process. Furthermore, under reasonable assumptions, we have rigorously derived upper bounds on the complexity of approximating parametric mappings with POD-DNN, thereby providing a theoretical analysis of the algorithm's empirical performance. | [
"['Guanhang Lei' 'Zhen Lei' 'Lei Shi' 'Chenyu Zeng']"
]
|
null | null | 2404.06846 | null | null | http://arxiv.org/pdf/2404.06846v1 | 2024-04-10T09:17:22Z | 2024-04-10T09:17:22Z | Register Your Forests: Decision Tree Ensemble Optimization by Explicit
CPU Register Allocation | Bringing high-level machine learning models to efficient and well-suited machine implementations often invokes a bunch of tools, e.g.~code generators, compilers, and optimizers. Along such tool chains, abstractions have to be applied. This leads to not optimally used CPU registers. This is a shortcoming, especially in resource constrained embedded setups. In this work, we present a code generation approach for decision tree ensembles, which produces machine assembly code within a single conversion step directly from the high-level model representation. Specifically, we develop various approaches to effectively allocate registers for the inference of decision tree ensembles. Extensive evaluations of the proposed method are conducted in comparison to the basic realization of C code from the high-level machine learning model and succeeding compilation. The results show that the performance of decision tree ensemble inference can be significantly improved (by up to $approx1.6times$), if the methods are applied carefully to the appropriate scenario. | [
"['Daniel Biebert' 'Christian Hakert' 'Kuan-Hsun Chen' 'Jian-Jia Chen']"
]
|
null | null | 2404.06856 | null | null | http://arxiv.org/pdf/2404.06856v1 | 2024-04-10T09:28:54Z | 2024-04-10T09:28:54Z | Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing | Modern computing systems heavily rely on hardware as the root of trust. However, their increasing complexity has given rise to security-critical vulnerabilities that cross-layer at-tacks can exploit. Traditional hardware vulnerability detection methods, such as random regression and formal verification, have limitations. Random regression, while scalable, is slow in exploring hardware, and formal verification techniques are often concerned with manual effort and state explosions. Hardware fuzzing has emerged as an effective approach to exploring and detecting security vulnerabilities in large-scale designs like modern processors. They outperform traditional methods regarding coverage, scalability, and efficiency. However, state-of-the-art fuzzers struggle to achieve comprehensive coverage of intricate hardware designs within a practical timeframe, often falling short of a 70% coverage threshold. We propose a novel ML-based hardware fuzzer, ChatFuzz, to address this challenge. Ourapproach leverages LLMs like ChatGPT to understand processor language, focusing on machine codes and generating assembly code sequences. RL is integrated to guide the input generation process by rewarding the inputs using code coverage metrics. We use the open-source RISCV-based RocketCore processor as our testbed. ChatFuzz achieves condition coverage rate of 75% in just 52 minutes compared to a state-of-the-art fuzzer, which requires a lengthy 30-hour window to reach a similar condition coverage. Furthermore, our fuzzer can attain 80% coverage when provided with a limited pool of 10 simulation instances/licenses within a 130-hour window. During this time, it conducted a total of 199K test cases, of which 6K produced discrepancies with the processor's golden model. Our analysis identified more than 10 unique mismatches, including two new bugs in the RocketCore and discrepancies from the RISC-V ISA Simulator. | [
"['Mohamadreza Rostami' 'Marco Chilese' 'Shaza Zeitouni' 'Rahul Kande'\n 'Jeyavijayan Rajendran' 'Ahmad-Reza Sadeghi']"
]
|
null | null | 2404.06869 | null | null | http://arxiv.org/pdf/2404.06869v1 | 2024-04-10T09:47:34Z | 2024-04-10T09:47:34Z | SleepPPG-Net2: Deep learning generalization for sleep staging from
photoplethysmography | Background: Sleep staging is a fundamental component in the diagnosis of sleep disorders and the management of sleep health. Traditionally, this analysis is conducted in clinical settings and involves a time-consuming scoring procedure. Recent data-driven algorithms for sleep staging, using the photoplethysmogram (PPG) time series, have shown high performance on local test sets but lower performance on external datasets due to data drift. Methods: This study aimed to develop a generalizable deep learning model for the task of four class (wake, light, deep, and rapid eye movement (REM)) sleep staging from raw PPG physiological time-series. Six sleep datasets, totaling 2,574 patients recordings, were used. In order to create a more generalizable representation, we developed and evaluated a deep learning model called SleepPPG-Net2, which employs a multi-source domain training approach.SleepPPG-Net2 was benchmarked against two state-of-the-art models. Results: SleepPPG-Net2 showed consistently higher performance over benchmark approaches, with generalization performance (Cohen's kappa) improving by up to 19%. Performance disparities were observed in relation to age, sex, and sleep apnea severity. Conclusion: SleepPPG-Net2 sets a new standard for staging sleep from raw PPG time-series. | [
"['Shirel Attia' 'Revital Shani Hershkovich' 'Alissa Tabakhov'\n 'Angeleene Ang' 'Sharon Haimov' 'Riva Tauman' 'Joachim A. Behar']"
]
|
null | null | 2404.06910 | null | null | http://arxiv.org/pdf/2404.06910v1 | 2024-04-10T11:03:17Z | 2024-04-10T11:03:17Z | Superposition Prompting: Improving and Accelerating Retrieval-Augmented
Generation | Despite the successes of large language models (LLMs), they exhibit significant drawbacks, particularly when processing long contexts. Their inference cost scales quadratically with respect to sequence length, making it expensive for deployment in some real-world text processing applications, such as retrieval-augmented generation (RAG). Additionally, LLMs also exhibit the "distraction phenomenon," where irrelevant context in the prompt degrades output quality. To address these drawbacks, we propose a novel RAG prompting methodology, superposition prompting, which can be directly applied to pre-trained transformer-based LLMs without the need for fine-tuning. At a high level, superposition prompting allows the LLM to process input documents in parallel prompt paths, discarding paths once they are deemed irrelevant. We demonstrate the capability of our method to simultaneously enhance time efficiency across a variety of question-answering benchmarks using multiple pre-trained LLMs. Furthermore, our technique significantly improves accuracy when the retrieved context is large relative the context the model was trained on. For example, our approach facilitates an 93x reduction in compute time while improving accuracy by 43% on the NaturalQuestions-Open dataset with the MPT-7B instruction-tuned model over naive RAG. | [
"['Thomas Merth' 'Qichen Fu' 'Mohammad Rastegari' 'Mahyar Najibi']"
]
|
null | null | 2404.06962 | null | null | http://arxiv.org/pdf/2404.06962v1 | 2024-04-10T12:22:03Z | 2024-04-10T12:22:03Z | Advancing Real-time Pandemic Forecasting Using Large Language Models: A
COVID-19 Case Study | Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performances and the provision of actionable insights for public health decision-makers. Our work introduces PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information that previously unattainable in traditional forecasting models. This approach, through a unique AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is subsequently tested across all 50 states of the U.S. Empirically, PandemicLLM is shown to be a high-performing pandemic forecasting framework that effectively captures the impact of emerging variants and can provide timely and accurate predictions. The proposed PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models. This study illuminates the potential of adapting LLMs and representation learning to enhance pandemic forecasting, illustrating how AI innovations can strengthen pandemic responses and crisis management in the future. | [
"['Hongru Du' 'Jianan Zhao' 'Yang Zhao' 'Shaochong Xu' 'Xihong Lin'\n 'Yiran Chen' 'Lauren M. Gardner' 'Hao Frank Yang']"
]
|
null | null | 2404.06966 | null | null | http://arxiv.org/pdf/2404.06966v1 | 2024-04-10T12:24:05Z | 2024-04-10T12:24:05Z | Are EEG Sequences Time Series? EEG Classification with Time Series
Models and Joint Subject Training | As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as with any other time series data. For EEG classification many models have been developed with layer types and architectures we typically do not see in time series classification. Furthermore, typically separate models for each individual subject are learned, not one model for all of them. In this paper, we systematically study the differences between EEG classification models and generic time series classification models. We describe three different model setups to deal with EEG data from different subjects, subject-specific models (most EEG literature), subject-agnostic models and subject-conditional models. In experiments on three datasets, we demonstrate that off-the-shelf time series classification models trained per subject perform close to EEG classification models, but that do not quite reach the performance of domain-specific modeling. Additionally, we combine time-series models with subject embeddings to train one joint subject-conditional classifier on all subjects. The resulting models are competitive with dedicated EEG models in 2 out of 3 datasets, even outperforming all EEG methods on one of them. | [
"['Johannes Burchert' 'Thorben Werner' 'Vijaya Krishna Yalavarthi'\n 'Diego Coello de Portugal' 'Maximilian Stubbemann' 'Lars Schmidt-Thieme']"
]
|
null | null | 2404.06969 | null | null | http://arxiv.org/pdf/2404.06969v2 | 2024-04-14T22:44:11Z | 2024-04-10T12:29:05Z | FiP: a Fixed-Point Approach for Causal Generative Modeling | Modeling true world data-generating processes lies at the heart of empirical science. Structural Causal Models (SCMs) and their associated Directed Acyclic Graphs (DAGs) provide an increasingly popular answer to such problems by defining the causal generative process that transforms random noise into observations. However, learning them from observational data poses an ill-posed and NP-hard inverse problem in general. In this work, we propose a new and equivalent formalism that does not require DAGs to describe them, viewed as fixed-point problems on the causally ordered variables, and we show three important cases where they can be uniquely recovered given the topological ordering (TO). To the best of our knowledge, we obtain the weakest conditions for their recovery when TO is known. Based on this, we design a two-stage causal generative model that first infers the causal order from observations in a zero-shot manner, thus by-passing the search, and then learns the generative fixed-point SCM on the ordered variables. To infer TOs from observations, we propose to amortize the learning of TOs on generated datasets by sequentially predicting the leaves of graphs seen during training. To learn fixed-point SCMs, we design a transformer-based architecture that exploits a new attention mechanism enabling the modeling of causal structures, and show that this parameterization is consistent with our formalism. Finally, we conduct an extensive evaluation of each method individually, and show that when combined, our model outperforms various baselines on generated out-of-distribution problems. | [
"['Meyer Scetbon' 'Joel Jennings' 'Agrin Hilmkil' 'Cheng Zhang' 'Chao Ma']"
]
|
null | null | 2404.06971 | null | null | http://arxiv.org/abs/2404.06971v1 | 2024-04-10T12:31:43Z | 2024-04-10T12:31:43Z | TrajPRed: Trajectory Prediction with Region-based Relation Learning | Forecasting human trajectories in traffic scenes is critical for safety within mixed or fully autonomous systems. Human future trajectories are driven by two major stimuli, social interactions, and stochastic goals. Thus, reliable forecasting needs to capture these two stimuli. Edge-based relation modeling represents social interactions using pairwise correlations from precise individual states. Nevertheless, edge-based relations can be vulnerable under perturbations. To alleviate these issues, we propose a region-based relation learning paradigm that models social interactions via region-wise dynamics of joint states, i.e., the changes in the density of crowds. In particular, region-wise agent joint information is encoded within convolutional feature grids. Social relations are modeled by relating the temporal changes of local joint information from a global perspective. We show that region-based relations are less susceptible to perturbations. In order to account for the stochastic individual goals, we exploit a conditional variational autoencoder to realize multi-goal estimation and diverse future prediction. Specifically, we perform variational inference via the latent distribution, which is conditioned on the correlation between input states and associated target goals. Sampling from the latent distribution enables the framework to reliably capture the stochastic behavior in test data. We integrate multi-goal estimation and region-based relation learning to model the two stimuli, social interactions, and stochastic goals, in a prediction framework. We evaluate our framework on the ETH-UCY dataset and Stanford Drone Dataset (SDD). We show that the diverse prediction better fits the ground truth when incorporating the relation module. Our framework outperforms the state-of-the-art models on SDD by $27.61%$/$18.20%$ of ADE/FDE metrics. | [
"['Chen Zhou' 'Ghassan AlRegib' 'Armin Parchami' 'Kunjan Singh']"
]
|
null | null | 2404.06972 | null | null | http://arxiv.org/abs/2404.06972v1 | 2024-04-10T12:32:18Z | 2024-04-10T12:32:18Z | Toward industrial use of continual learning : new metrics proposal for
class incremental learning | In this paper, we investigate continual learning performance metrics used in class incremental learning strategies for continual learning (CL) using some high performing methods. We investigate especially mean task accuracy. First, we show that it lacks of expressiveness through some simple experiments to capture performance. We show that monitoring average tasks performance is over optimistic and can lead to misleading conclusions for future real life industrial uses. Then, we propose first a simple metric, Minimal Incremental Class Accuracy (MICA) which gives a fair and more useful evaluation of different continual learning methods. Moreover, in order to provide a simple way to easily compare different methods performance in continual learning, we derive another single scalar metric that take into account the learning performance variation as well as our newly introduced metric. | [
"['Konaté Mohamed Abbas' 'Anne-Françoise Yao' 'Thierry Chateau'\n 'Pierre Bouges']"
]
|
null | null | 2404.06975 | null | null | http://arxiv.org/pdf/2404.06975v1 | 2024-04-10T13:49:20Z | 2024-04-10T13:49:20Z | Multi-Agent Soft Actor-Critic with Global Loss for Autonomous
Mobility-on-Demand Fleet Control | We study a sequential decision-making problem for a profit-maximizing operator of an Autonomous Mobility-on-Demand system. Optimizing a central operator's vehicle-to-request dispatching policy requires efficient and effective fleet control strategies. To this end, we employ a multi-agent Soft Actor-Critic algorithm combined with weighted bipartite matching. We propose a novel vehicle-based algorithm architecture and adapt the critic's loss function to appropriately consider global actions. Furthermore, we extend our algorithm to incorporate rebalancing capabilities. Through numerical experiments, we show that our approach outperforms state-of-the-art benchmarks by up to 12.9% for dispatching and up to 38.9% with integrated rebalancing. | [
"['Zeno Woywood' 'Jasper I. Wiltfang' 'Julius Luy' 'Tobias Enders'\n 'Maximilian Schiffer']"
]
|
null | null | 2404.06978 | null | null | http://arxiv.org/pdf/2404.06978v1 | 2024-04-10T12:48:10Z | 2024-04-10T12:48:10Z | The CAST package for training and assessment of spatial prediction
models in R | One key task in environmental science is to map environmental variables continuously in space or even in space and time. Machine learning algorithms are frequently used to learn from local field observations to make spatial predictions by estimating the value of the variable of interest in places where it has not been measured. However, the application of machine learning strategies for spatial mapping involves additional challenges compared to "non-spatial" prediction tasks that often originate from spatial autocorrelation and from training data that are not independent and identically distributed. In the past few years, we developed a number of methods to support the application of machine learning for spatial data which involves the development of suitable cross-validation strategies for performance assessment and model selection, spatial feature selection, and methods to assess the area of applicability of the trained models. The intention of the CAST package is to support the application of machine learning strategies for predictive mapping by implementing such methods and making them available for easy integration into modelling workflows. Here we introduce the CAST package and its core functionalities. At the case study of mapping plant species richness, we will go through the different steps of the modelling workflow and show how CAST can be used to support more reliable spatial predictions. | [
"['Hanna Meyer' 'Marvin Ludwig' 'Carles Milà' 'Jan Linnenbrink'\n 'Fabian Schumacher']"
]
|
null | null | 2404.06989 | null | null | http://arxiv.org/pdf/2404.06989v1 | 2024-04-10T13:08:07Z | 2024-04-10T13:08:07Z | On Fixing the Right Problems in Predictive Analytics: AUC Is Not the
Problem | Recently, ACM FAccT published an article by Kwegyir-Aggrey and colleagues (2023), critiquing the use of AUC ROC in predictive analytics in several domains. In this article, we offer a critique of that article. Specifically, we highlight technical inaccuracies in that paper's comparison of metrics, mis-specification of the interpretation and goals of AUC ROC, the article's use of the accuracy metric as a gold standard for comparison to AUC ROC, and the article's application of critiques solely to AUC ROC for concerns that would apply to the use of any metric. We conclude with a re-framing of the very valid concerns raised in that article, and discuss how the use of AUC ROC can remain a valid and appropriate practice in a well-informed predictive analytics approach taking those concerns into account. We conclude by discussing the combined use of multiple metrics, including machine learning bias metrics, and AUC ROC's place in such an approach. Like broccoli, AUC ROC is healthy, but also like broccoli, researchers and practitioners in our field shouldn't eat a diet of only AUC ROC. | [
"['Ryan S. Baker' 'Nigel Bosch' 'Stephen Hutt' 'Andres F. Zambrano'\n 'Alex J. Bowers']"
]
|
null | null | 2404.06993 | null | null | http://arxiv.org/pdf/2404.06993v1 | 2024-04-10T13:12:07Z | 2024-04-10T13:12:07Z | Quiver Laplacians and Feature Selection | The challenge of selecting the most relevant features of a given dataset arises ubiquitously in data analysis and dimensionality reduction. However, features found to be of high importance for the entire dataset may not be relevant to subsets of interest, and vice versa. Given a feature selector and a fixed decomposition of the data into subsets, we describe a method for identifying selected features which are compatible with the decomposition into subsets. We achieve this by re-framing the problem of finding compatible features to one of finding sections of a suitable quiver representation. In order to approximate such sections, we then introduce a Laplacian operator for quiver representations valued in Hilbert spaces. We provide explicit bounds on how the spectrum of a quiver Laplacian changes when the representation and the underlying quiver are modified in certain natural ways. Finally, we apply this machinery to the study of peak-calling algorithms which measure chromatin accessibility in single-cell data. We demonstrate that eigenvectors of the associated quiver Laplacian yield locally and globally compatible features. | [
"['Otto Sumray' 'Heather A. Harrington' 'Vidit Nanda']"
]
|
null | null | 2404.06997 | null | null | http://arxiv.org/pdf/2404.06997v1 | 2024-04-10T13:24:27Z | 2024-04-10T13:24:27Z | Agent-driven Generative Semantic Communication for Remote Surveillance | In the era of 6G, featuring compelling visions of intelligent transportation system, digital twins, remote surveillance is poised to become a ubiquitous practice. The substantial data volume and frequent updates present challenges in wireless networks. To address this, we propose a novel agent-driven generative semantic communication (A-GSC) framework based on reinforcement learning. In contrast to the existing research on semantic communication (SemCom), which mainly focuses on semantic compression or semantic sampling, we seamlessly cascade both together by jointly considering the intrinsic attributes of source information and the contextual information regarding the task. Notably, the introduction of the generative artificial intelligence (GAI) enables the independent design of semantic encoders and decoders. In this work, we develop an agent-assisted semantic encoder leveraging the knowledge based soft actor-critic algorithm, which can track the semantic changes, channel condition, and sampling intervals, so as to perform adaptive semantic sampling. Accordingly, we design a semantic decoder with both predictive and generative capabilities, which consists of two tailored modules. Moreover, the effectiveness of the designed models has been verified based on the dataset generated from CDNet2014, and the performance gain of the overall A-GSC framework in both energy saving and reconstruction accuracy have been demonstrated. | [
"['Wanting Yang' 'Zehui Xiong' 'Yanli Yuan' 'Wenchao Jiang'\n 'Tony Q. S. Quek' 'Merouane Debbah']"
]
|
null | null | 2404.07008 | null | null | http://arxiv.org/pdf/2404.07008v1 | 2024-04-10T13:47:22Z | 2024-04-10T13:47:22Z | Knowledge graphs for empirical concept retrieval | Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user, viz. as a tool for personalized explainability. An important class of concept-based explainability methods is constructed with empirically defined concepts, indirectly defined through a set of positive and negative examples, as in the TCAV approach (Kim et al., 2018). While it is appealing to the user to avoid formal definitions of concepts and their operationalization, it can be challenging to establish relevant concept datasets. Here, we address this challenge using general knowledge graphs (such as, e.g., Wikidata or WordNet) for comprehensive concept definition and present a workflow for user-driven data collection in both text and image domains. The concepts derived from knowledge graphs are defined interactively, providing an opportunity for personalization and ensuring that the concepts reflect the user's intentions. We test the retrieved concept datasets on two concept-based explainability methods, namely concept activation vectors (CAVs) and concept activation regions (CARs) (Crabbe and van der Schaar, 2022). We show that CAVs and CARs based on these empirical concept datasets provide robust and accurate explanations. Importantly, we also find good alignment between the models' representations of concepts and the structure of knowledge graphs, i.e., human representations. This supports our conclusion that knowledge graph-based concepts are relevant for XAI. | [
"['Lenka Tětková' 'Teresa Karen Scheidt' 'Maria Mandrup Fogh'\n 'Ellen Marie Gaunby Jørgensen' 'Finn Årup Nielsen' 'Lars Kai Hansen']"
]
|
null | null | 2404.07009 | null | null | http://arxiv.org/pdf/2404.07009v3 | 2024-05-15T18:05:54Z | 2024-04-10T13:50:46Z | A Mathematical Theory for Learning Semantic Languages by Abstract
Learners | Recent advances in Large Language Models (LLMs) have demonstrated the emergence of capabilities (learned skills) when the number of system parameters and the size of training data surpass certain thresholds. The exact mechanisms behind such phenomena are not fully understood and remain a topic of active research. Inspired by the skill-text bipartite graph model proposed by Arora and Goyal for modeling semantic languages, we develop a mathematical theory to explain the emergence of learned skills, taking the learning (or training) process into account. Our approach models the learning process for skills in the skill-text bipartite graph as an iterative decoding process in Low-Density Parity Check (LDPC) codes and Irregular Repetition Slotted ALOHA (IRSA). Using density evolution analysis, we demonstrate the emergence of learned skills when the ratio of the number of training texts to the number of skills exceeds a certain threshold. Our analysis also yields a scaling law for testing errors relative to this ratio. Upon completion of the training, the association of learned skills can also be acquired to form a skill association graph. We use site percolation analysis to derive the conditions for the existence of a giant component in the skill association graph. Our analysis can also be extended to the setting with a hierarchy of skills, where a fine-tuned model is built upon a foundation model. It is also applicable to the setting with multiple classes of skills and texts. As an important application, we propose a method for semantic compression and discuss its connections to semantic communication. | [
"['Kuo-Yu Liao' 'Cheng-Shang Chang' 'Y. -W. Peter Hong']"
]
|
null | null | 2404.07046 | null | null | http://arxiv.org/pdf/2404.07046v1 | 2024-04-10T14:36:35Z | 2024-04-10T14:36:35Z | Comparison of decision trees with Local Interpretable Model-Agnostic
Explanations (LIME) technique and multi-linear regression for explaining
support vector regression model in terms of root mean square error (RMSE)
values | In this work the decision trees are used for explanation of support vector regression model. The decision trees act as a global technique as well as a local technique. They are compared against the popular technique of LIME which is a local explanatory technique and with multi linear regression. It is observed that decision trees give a lower RMSE value when fitted to support vector regression as compared to LIME in 87% of the runs over 5 datasets. The comparison of results is statistically significant. Multi linear regression also gives a lower RMSE value when fitted to support vector regression model as compared to LIME in 73% of the runs over 5 datasets but the comparison of results is not statistically significant. Also, when used as a local explanatory technique, decision trees give better performance than LIME and the comparison of results is statistically significant. | [
"['Amit Thombre']"
]
|
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