categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.03714 | null | null | http://arxiv.org/pdf/2405.03714v1 | 2024-05-04T17:27:51Z | 2024-05-04T17:27:51Z | UniDEC : Unified Dual Encoder and Classifier Training for Extreme
Multi-Label Classification | Extreme Multi-label Classification (XMC) involves predicting a subset of relevant labels from an extremely large label space, given an input query and labels with textual features. Models developed for this problem have conventionally used modular approach with (i) a Dual Encoder (DE) to embed the queries and label texts, (ii) a One-vs-All classifier to rerank the shortlisted labels mined through meta-classifier training. While such methods have shown empirical success, we observe two key uncharted aspects, (i) DE training typically uses only a single positive relation even for datasets which offer more, (ii) existing approaches fixate on using only OvA reduction of the multi-label problem. This work aims to explore these aspects by proposing UniDEC, a novel end-to-end trainable framework which trains the dual encoder and classifier in together in a unified fashion using a multi-class loss. For the choice of multi-class loss, the work proposes a novel pick-some-label (PSL) reduction of the multi-label problem with leverages multiple (in come cases, all) positives. The proposed framework achieves state-of-the-art results on a single GPU, while achieving on par results with respect to multi-GPU SOTA methods on various XML benchmark datasets, all while using 4-16x lesser compute and being practically scalable even beyond million label scale datasets. | [
"['Siddhant Kharbanda' 'Devaansh Gupta' 'Gururaj K' 'Pankaj Malhotra'\n 'Cho-Jui Hsieh' 'Rohit Babbar']"
]
|
null | null | 2405.03718 | null | null | http://arxiv.org/pdf/2405.03718v1 | 2024-05-05T16:38:04Z | 2024-05-05T16:38:04Z | A Single Online Agent Can Efficiently Learn Mean Field Games | Mean field games (MFGs) are a promising framework for modeling the behavior of large-population systems. However, solving MFGs can be challenging due to the coupling of forward population evolution and backward agent dynamics. Typically, obtaining mean field Nash equilibria (MFNE) involves an iterative approach where the forward and backward processes are solved alternately, known as fixed-point iteration (FPI). This method requires fully observed population propagation and agent dynamics over the entire spatial domain, which could be impractical in some real-world scenarios. To overcome this limitation, this paper introduces a novel online single-agent model-free learning scheme, which enables a single agent to learn MFNE using online samples, without prior knowledge of the state-action space, reward function, or transition dynamics. Specifically, the agent updates its policy through the value function (Q), while simultaneously evaluating the mean field state (M), using the same batch of observations. We develop two variants of this learning scheme: off-policy and on-policy QM iteration. We prove that they efficiently approximate FPI, and a sample complexity guarantee is provided. The efficacy of our methods is confirmed by numerical experiments. | [
"['Chenyu Zhang' 'Xu Chen' 'Xuan Di']"
]
|
null | null | 2405.03720 | null | null | http://arxiv.org/pdf/2405.03720v1 | 2024-05-05T20:39:15Z | 2024-05-05T20:39:15Z | Spatial Transfer Learning with Simple MLP | First step to investigate the potential of transfer learning applied to the field of spatial statistics | [
"['Hongjian Yang']"
]
|
null | null | 2405.03723 | null | null | http://arxiv.org/pdf/2405.03723v1 | 2024-05-06T03:30:02Z | 2024-05-06T03:30:02Z | Generative adversarial learning with optimal input dimension and its
adaptive generator architecture | We investigate the impact of the input dimension on the generalization error in generative adversarial networks (GANs). In particular, we first provide both theoretical and practical evidence to validate the existence of an optimal input dimension (OID) that minimizes the generalization error. Then, to identify the OID, we introduce a novel framework called generalized GANs (G-GANs), which includes existing GANs as a special case. By incorporating the group penalty and the architecture penalty developed in the paper, G-GANs have several intriguing features. First, our framework offers adaptive dimensionality reduction from the initial dimension to a dimension necessary for generating the target distribution. Second, this reduction in dimensionality also shrinks the required size of the generator network architecture, which is automatically identified by the proposed architecture penalty. Both reductions in dimensionality and the generator network significantly improve the stability and the accuracy of the estimation and prediction. Theoretical support for the consistent selection of the input dimension and the generator network is provided. Third, the proposed algorithm involves an end-to-end training process, and the algorithm allows for dynamic adjustments between the input dimension and the generator network during training, further enhancing the overall performance of G-GANs. Extensive experiments conducted with simulated and benchmark data demonstrate the superior performance of G-GANs. In particular, compared to that of off-the-shelf methods, G-GANs achieves an average improvement of 45.68% in the CT slice dataset, 43.22% in the MNIST dataset and 46.94% in the FashionMNIST dataset in terms of the maximum mean discrepancy or Frechet inception distance. Moreover, the features generated based on the input dimensions identified by G-GANs align with visually significant features. | [
"['Zhiyao Tan' 'Ling Zhou' 'Huazhen Lin']"
]
|
null | null | 2405.03724 | null | null | http://arxiv.org/pdf/2405.03724v1 | 2024-05-06T04:00:00Z | 2024-05-06T04:00:00Z | GraphSL: An Open-Source Library for Graph Source Localization Approaches
and Benchmark Datasets | We present GraphSL, a novel library designed for investigating the graph source localization problem. Our library facilitates the exploration of various graph diffusion models for simulating information spread and enables the evaluation of cutting-edge source localization approaches on established benchmark datasets. The source code of GraphSL is made available at url{https://github.com/xianggebenben/GraphSL}. Bug reports and feedback can be directed to the Github issues page (url{https://github.com/xianggebenben/GraphSL/issues}). | [
"['Junxiang Wang' 'Liang Zhao']"
]
|
null | null | 2405.03725 | null | null | http://arxiv.org/pdf/2405.03725v1 | 2024-05-06T06:17:16Z | 2024-05-06T06:17:16Z | Deep Oscillatory Neural Network | We propose a novel, brain-inspired deep neural network model known as the Deep Oscillatory Neural Network (DONN). Deep neural networks like the Recurrent Neural Networks indeed possess sequence processing capabilities but the internal states of the network are not designed to exhibit brain-like oscillatory activity. With this motivation, the DONN is designed to have oscillatory internal dynamics. Neurons of the DONN are either nonlinear neural oscillators or traditional neurons with sigmoidal or ReLU activation. The neural oscillator used in the model is the Hopf oscillator, with the dynamics described in the complex domain. Input can be presented to the neural oscillator in three possible modes. The sigmoid and ReLU neurons also use complex-valued extensions. All the weight stages are also complex-valued. Training follows the general principle of weight change by minimizing the output error and therefore has an overall resemblance to complex backpropagation. A generalization of DONN to convolutional networks known as the Oscillatory Convolutional Neural Network is also proposed. The two proposed oscillatory networks are applied to a variety of benchmark problems in signal and image/video processing. The performance of the proposed models is either comparable or superior to published results on the same data sets. | [
"['Nurani Rajagopal Rohan' 'Vigneswaran C' 'Sayan Ghosh'\n 'Kishore Rajendran' 'Gaurav A' 'V Srinivasa Chakravarthy']"
]
|
null | null | 2405.03726 | null | null | http://arxiv.org/pdf/2405.03726v1 | 2024-05-06T06:46:11Z | 2024-05-06T06:46:11Z | sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass
Transport on the Manifold of Gaussian Mixtures | Influenced by breakthroughs in LLMs, single-cell foundation models are emerging. While these models show successful performance in cell type clustering, phenotype classification, and gene perturbation response prediction, it remains to be seen if a simpler model could achieve comparable or better results, especially with limited data. This is important, as the quantity and quality of single-cell data typically fall short of the standards in textual data used for training LLMs. Single-cell sequencing often suffers from technical artifacts, dropout events, and batch effects. These challenges are compounded in a weakly supervised setting, where the labels of cell states can be noisy, further complicating the analysis. To tackle these challenges, we present sc-OTGM, streamlined with less than 500K parameters, making it approximately 100x more compact than the foundation models, offering an efficient alternative. sc-OTGM is an unsupervised model grounded in the inductive bias that the scRNAseq data can be generated from a combination of the finite multivariate Gaussian distributions. The core function of sc-OTGM is to create a probabilistic latent space utilizing a GMM as its prior distribution and distinguish between distinct cell populations by learning their respective marginal PDFs. It uses a Hit-and-Run Markov chain sampler to determine the OT plan across these PDFs within the GMM framework. We evaluated our model against a CRISPR-mediated perturbation dataset, called CROP-seq, consisting of 57 one-gene perturbations. Our results demonstrate that sc-OTGM is effective in cell state classification, aids in the analysis of differential gene expression, and ranks genes for target identification through a recommender system. It also predicts the effects of single-gene perturbations on downstream gene regulation and generates synthetic scRNA-seq data conditioned on specific cell states. | [
"['Andac Demir' 'Elizaveta Solovyeva' 'James Boylan' 'Mei Xiao'\n 'Fabrizio Serluca' 'Sebastian Hoersch' 'Jeremy Jenkins'\n 'Murthy Devarakonda' 'Bulent Kiziltan']"
]
|
null | null | 2405.03727 | null | null | http://arxiv.org/pdf/2405.03727v2 | 2024-05-11T07:49:39Z | 2024-05-06T08:09:46Z | Large Language Models Synergize with Automated Machine Learning | Recently, program synthesis driven by large language models (LLMs) has become increasingly popular. However, program synthesis for machine learning (ML) tasks still poses significant challenges. This paper explores a novel form of program synthesis, targeting ML programs, by combining LLMs and automated machine learning (autoML). Specifically, our goal is to fully automate the generation and optimization of the code of the entire ML workflow, from data preparation to modeling and post-processing, utilizing only textual descriptions of the ML tasks. To manage the length and diversity of ML programs, we propose to break each ML program into smaller, manageable parts. Each part is generated separately by the LLM, with careful consideration of their compatibilities. To ensure compatibilities, we design a testing technique for ML programs. Unlike traditional program synthesis, which typically relies on binary evaluations (i.e., correct or incorrect), evaluating ML programs necessitates more than just binary judgments. Therefore, we further assess ML programs numerically and select the optimal programs from a range of candidates using AutoML methods. In experiments across various ML tasks, our method outperforms existing methods in 10 out of 12 tasks for generating ML programs. In addition, autoML significantly improves the performance of the generated ML programs. In experiments, given the textual task description, our method, Text-to-ML, generates the complete and optimized ML program in a fully autonomous process. | [
"['Jinglue Xu' 'Jialong Li' 'Zhen Liu'\n 'Nagar Anthel Venkatesh Suryanarayanan' 'Guoyuan Zhou' 'Jia Guo'\n 'Hitoshi Iba' 'Kenji Tei']"
]
|
null | null | 2405.03730 | null | null | http://arxiv.org/pdf/2405.03730v2 | 2024-05-27T12:09:08Z | 2024-05-06T09:47:29Z | Tilt your Head: Activating the Hidden Spatial-Invariance of Classifiers | Deep neural networks are applied in more and more areas of everyday life. However, they still lack essential abilities, such as robustly dealing with spatially transformed input signals. Approaches to mitigate this severe robustness issue are limited to two pathways: Either models are implicitly regularised by increased sample variability (data augmentation) or explicitly constrained by hard-coded inductive biases. The limiting factor of the former is the size of the data space, which renders sufficient sample coverage intractable. The latter is limited by the engineering effort required to develop such inductive biases for every possible scenario. Instead, we take inspiration from human behaviour, where percepts are modified by mental or physical actions during inference. We propose a novel technique to emulate such an inference process for neural nets. This is achieved by traversing a sparsified inverse transformation tree during inference using parallel energy-based evaluations. Our proposed inference algorithm, called Inverse Transformation Search (ITS), is model-agnostic and equips the model with zero-shot pseudo-invariance to spatially transformed inputs. We evaluated our method on several benchmark datasets, including a synthesised ImageNet test set. ITS outperforms the utilised baselines on all zero-shot test scenarios. | [
"['Johann Schmidt' 'Sebastian Stober']"
]
|
null | null | 2405.03732 | null | null | http://arxiv.org/pdf/2405.03732v1 | 2024-05-06T10:53:13Z | 2024-05-06T10:53:13Z | Accelerated MR Cholangiopancreatography with Deep Learning-based
Reconstruction | This study accelerates MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3T and 0.55T. Thirty healthy volunteers underwent conventional two-fold MRCP scans at field strengths of 3T or 0.55T. We trained a variational network (VN) using retrospectively six-fold undersampled data obtained at 3T. We then evaluated our method against standard techniques such as parallel imaging (PI) and compressed sensing (CS), focusing on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as metrics. Furthermore, considering acquiring fully-sampled MRCP is impractical, we added a self-supervised DL reconstruction (SSDU) to the evaluating group. We also tested our method in a prospective accelerated scenario to reflect real-world clinical applications and evaluated its adaptability to MRCP at 0.55T. Our method demonstrated a remarkable reduction of average acquisition time from 599/542 to 255/180 seconds for MRCP at 3T/0.55T. In both retrospective and prospective undersampling scenarios, the PSNR and SSIM of VN were higher than those of PI, CS, and SSDU. At the same time, VN preserved the image quality of undersampled data, i.e., sharpness and the visibility of hepatobiliary ducts. In addition, VN also produced high quality reconstructions at 0.55T resulting in the highest PSNR and SSIM. In summary, VN trained for highly accelerated MRCP allows to reduce the acquisition time by a factor of 2.4/3.0 at 3T/0.55T while maintaining the image quality of the conventional acquisition. | [
"['Jinho Kim' 'Marcel Dominik Nickel' 'Florian Knoll']"
]
|
null | null | 2405.03735 | null | null | http://arxiv.org/pdf/2405.03735v1 | 2024-05-06T15:48:24Z | 2024-05-06T15:48:24Z | Select to Perfect: Imitating desired behavior from large multi-agent
data | AI agents are commonly trained with large datasets of demonstrations of human behavior. However, not all behaviors are equally safe or desirable. Desired characteristics for an AI agent can be expressed by assigning desirability scores, which we assume are not assigned to individual behaviors but to collective trajectories. For example, in a dataset of vehicle interactions, these scores might relate to the number of incidents that occurred. We first assess the effect of each individual agent's behavior on the collective desirability score, e.g., assessing how likely an agent is to cause incidents. This allows us to selectively imitate agents with a positive effect, e.g., only imitating agents that are unlikely to cause incidents. To enable this, we propose the concept of an agent's Exchange Value, which quantifies an individual agent's contribution to the collective desirability score. The Exchange Value is the expected change in desirability score when substituting the agent for a randomly selected agent. We propose additional methods for estimating Exchange Values from real-world datasets, enabling us to learn desired imitation policies that outperform relevant baselines. The project website can be found at https://tinyurl.com/select-to-perfect. | [
"['Tim Franzmeyer' 'Edith Elkind' 'Philip Torr' 'Jakob Foerster'\n 'Joao Henriques']"
]
|
null | null | 2405.03777 | null | null | http://arxiv.org/pdf/2405.03777v1 | 2024-05-06T18:19:01Z | 2024-05-06T18:19:01Z | Is ReLU Adversarially Robust? | The efficacy of deep learning models has been called into question by the presence of adversarial examples. Addressing the vulnerability of deep learning models to adversarial examples is crucial for ensuring their continued development and deployment. In this work, we focus on the role of rectified linear unit (ReLU) activation functions in the generation of adversarial examples. ReLU functions are commonly used in deep learning models because they facilitate the training process. However, our empirical analysis demonstrates that ReLU functions are not robust against adversarial examples. We propose a modified version of the ReLU function, which improves robustness against adversarial examples. Our results are supported by an experiment, which confirms the effectiveness of our proposed modification. Additionally, we demonstrate that applying adversarial training to our customized model further enhances its robustness compared to a general model. | [
"['Korn Sooksatra' 'Greg Hamerly' 'Pablo Rivas']"
]
|
null | null | 2405.03782 | null | null | http://arxiv.org/pdf/2405.03782v1 | 2024-05-06T18:21:41Z | 2024-05-06T18:21:41Z | Interpretable Data Fusion for Distributed Learning: A Representative
Approach via Gradient Matching | This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation. Unlike traditional distributed learning methods such as Federated Learning, which do not offer human interpretability, our method makes complex machine learning processes accessible and comprehensible. It achieves this by condensing extensive datasets into digestible formats, thus fostering intuitive human-machine interactions. Additionally, this approach maintains privacy and communication efficiency, and it matches the training performance of models using raw data. Simulation results show that our approach is competitive with or outperforms traditional Federated Learning in accuracy and convergence, especially in scenarios with complex models and a higher number of clients. This framework marks a step forward in integrating human intuition with machine intelligence, which potentially enhances human-machine learning interfaces and collaborative efforts. | [
"['Mengchen Fan' 'Baocheng Geng' 'Keren Li' 'Xueqian Wang'\n 'Pramod K. Varshney']"
]
|
null | null | 2405.03789 | null | null | http://arxiv.org/pdf/2405.03789v1 | 2024-05-06T18:45:18Z | 2024-05-06T18:45:18Z | On Adversarial Examples for Text Classification by Perturbing Latent
Representations | Recently, with the advancement of deep learning, several applications in text classification have advanced significantly. However, this improvement comes with a cost because deep learning is vulnerable to adversarial examples. This weakness indicates that deep learning is not very robust. Fortunately, the input of a text classifier is discrete. Hence, it can prevent the classifier from state-of-the-art attacks. Nonetheless, previous works have generated black-box attacks that successfully manipulate the discrete values of the input to find adversarial examples. Therefore, instead of changing the discrete values, we transform the input into its embedding vector containing real values to perform the state-of-the-art white-box attacks. Then, we convert the perturbed embedding vector back into a text and name it an adversarial example. In summary, we create a framework that measures the robustness of a text classifier by using the gradients of the classifier. | [
"['Korn Sooksatra' 'Bikram Khanal' 'Pablo Rivas']"
]
|
null | null | 2405.03799 | null | null | http://arxiv.org/pdf/2405.03799v1 | 2024-05-06T19:09:37Z | 2024-05-06T19:09:37Z | Synthetic Data from Diffusion Models Improve Drug Discovery Prediction | Artificial intelligence (AI) is increasingly used in every stage of drug development. Continuing breakthroughs in AI-based methods for drug discovery require the creation, improvement, and refinement of drug discovery data. We posit a new data challenge that slows the advancement of drug discovery AI: datasets are often collected independently from each other, often with little overlap, creating data sparsity. Data sparsity makes data curation difficult for researchers looking to answer key research questions requiring values posed across multiple datasets. We propose a novel diffusion GNN model Syngand capable of generating ligand and pharmacokinetic data end-to-end. We show and provide a methodology for sampling pharmacokinetic data for existing ligands using our Syngand model. We show the initial promising results on the efficacy of the Syngand-generated synthetic target property data on downstream regression tasks with AqSolDB, LD50, and hERG central. Using our proposed model and methodology, researchers can easily generate synthetic ligand data to help them explore research questions that require data spanning multiple datasets. | [
"['Bing Hu' 'Ashish Saragadam' 'Anita Layton' 'Helen Chen']"
]
|
null | null | 2405.03807 | null | null | http://arxiv.org/pdf/2405.03807v1 | 2024-05-06T19:31:25Z | 2024-05-06T19:31:25Z | UniGen: Unified Modeling of Initial Agent States and Trajectories for
Generating Autonomous Driving Scenarios | This paper introduces UniGen, a novel approach to generating new traffic scenarios for evaluating and improving autonomous driving software through simulation. Our approach models all driving scenario elements in a unified model: the position of new agents, their initial state, and their future motion trajectories. By predicting the distributions of all these variables from a shared global scenario embedding, we ensure that the final generated scenario is fully conditioned on all available context in the existing scene. Our unified modeling approach, combined with autoregressive agent injection, conditions the placement and motion trajectory of every new agent on all existing agents and their trajectories, leading to realistic scenarios with low collision rates. Our experimental results show that UniGen outperforms prior state of the art on the Waymo Open Motion Dataset. | [
"['Reza Mahjourian' 'Rongbing Mu' 'Valerii Likhosherstov' 'Paul Mougin'\n 'Xiukun Huang' 'Joao Messias' 'Shimon Whiteson']"
]
|
null | null | 2405.03840 | null | null | http://arxiv.org/pdf/2405.03840v1 | 2024-05-06T20:43:02Z | 2024-05-06T20:43:02Z | End-to-End Autoencoder for Drill String Acoustic Communications | Drill string communications are important for drilling efficiency and safety. The design of a low latency drill string communication system with high throughput and reliability remains an open challenge. In this paper a deep learning autoencoder (AE) based end-to-end communication system, where transmitter and receiver implemented as feed forward neural networks, is proposed for acousticdrill string communications. Simulation shows that the AE system is able to outperform a baseline non-contiguous OFDM system in terms of BER and PAPR, operating with lower latency. | [
"['Iurii Lezhenin' 'Aleksandr Sidnev' 'Vladimir Tsygan' 'Igor Malyshev']"
]
|
null | null | 2405.03848 | null | null | http://arxiv.org/pdf/2405.03848v1 | 2024-05-02T16:31:09Z | 2024-05-02T16:31:09Z | CityLearn v2: Energy-flexible, resilient, occupant-centric, and
carbon-aware management of grid-interactive communities | As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide on a community scale, particularly to understand the impact of geographic, climatic, and occupant behavioral differences on their effectiveness, as well as identify the best control strategies to accelerate their real-world adoption. CityLearn provides an environment for benchmarking simple and advanced distributed energy resource control algorithms including rule-based, model-predictive, and reinforcement learning control. CityLearn v2 presented here extends CityLearn v1 by providing a simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create virtual grid-interactive communities for resilient, multi-agent distributed energy resources and objective control with dynamic occupant feedback. This work details the v2 environment design and provides application examples that utilize reinforcement learning to manage battery energy storage system charging/discharging cycles, vehicle-to-grid control, and thermal comfort during heat pump power modulation. | [
"['Kingsley Nweye' 'Kathryn Kaspar' 'Giacomo Buscemi' 'Tiago Fonseca'\n 'Giuseppe Pinto' 'Dipanjan Ghose' 'Satvik Duddukuru' 'Pavani Pratapa'\n 'Han Li' 'Javad Mohammadi' 'Luis Lino Ferreira' 'Tianzhen Hong'\n 'Mohamed Ouf' 'Alfonso Capozzoli' 'Zoltan Nagy']"
]
|
null | null | 2405.03865 | null | null | http://arxiv.org/pdf/2405.03865v1 | 2024-05-06T21:25:51Z | 2024-05-06T21:25:51Z | Information-driven Affordance Discovery for Efficient Robotic
Manipulation | Robotic affordances, providing information about what actions can be taken in a given situation, can aid robotic manipulation. However, learning about affordances requires expensive large annotated datasets of interactions or demonstrations. In this work, we argue that well-directed interactions with the environment can mitigate this problem and propose an information-based measure to augment the agent's objective and accelerate the affordance discovery process. We provide a theoretical justification of our approach and we empirically validate the approach both in simulation and real-world tasks. Our method, which we dub IDA, enables the efficient discovery of visual affordances for several action primitives, such as grasping, stacking objects, or opening drawers, strongly improving data efficiency in simulation, and it allows us to learn grasping affordances in a small number of interactions, on a real-world setup with a UFACTORY XArm 6 robot arm. | [
"['Pietro Mazzaglia' 'Taco Cohen' 'Daniel Dijkman']"
]
|
null | null | 2405.03869 | null | null | http://arxiv.org/pdf/2405.03869v2 | 2024-05-12T20:20:57Z | 2024-05-06T21:34:46Z | Outlier Gradient Analysis: Efficiently Improving Deep Learning Model
Performance via Hessian-Free Influence Functions | Influence functions offer a robust framework for assessing the impact of each training data sample on model predictions, serving as a prominent tool in data-centric learning. Despite their widespread use in various tasks, the strong convexity assumption on the model and the computational cost associated with calculating the inverse of the Hessian matrix pose constraints, particularly when analyzing large deep models. This paper focuses on a classical data-centric scenario--trimming detrimental samples--and addresses both challenges within a unified framework. Specifically, we establish an equivalence transformation between identifying detrimental training samples via influence functions and outlier gradient detection. This transformation not only presents a straightforward and Hessian-free formulation but also provides profound insights into the role of the gradient in sample impact. Moreover, it relaxes the convexity assumption of influence functions, extending their applicability to non-convex deep models. Through systematic empirical evaluations, we first validate the correctness of our proposed outlier gradient analysis on synthetic datasets and then demonstrate its effectiveness in detecting mislabeled samples in vision models, selecting data samples for improving performance of transformer models for natural language processing, and identifying influential samples for fine-tuned Large Language Models. | [
"['Anshuman Chhabra' 'Bo Li' 'Jian Chen' 'Prasant Mohapatra' 'Hongfu Liu']"
]
|
null | null | 2405.03875 | null | null | http://arxiv.org/pdf/2405.03875v1 | 2024-05-06T21:46:10Z | 2024-05-06T21:46:10Z | Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits | Data Shapley provides a principled approach to data valuation and plays a crucial role in data-centric machine learning (ML) research. Data selection is considered a standard application of Data Shapley. However, its data selection performance has shown to be inconsistent across settings in the literature. This study aims to deepen our understanding of this phenomenon. We introduce a hypothesis testing framework and show that Data Shapley's performance can be no better than random selection without specific constraints on utility functions. We identify a class of utility functions, monotonically transformed modular functions, within which Data Shapley optimally selects data. Based on this insight, we propose a heuristic for predicting Data Shapley's effectiveness in data selection tasks. Our experiments corroborate these findings, adding new insights into when Data Shapley may or may not succeed. | [
"['Jiachen T. Wang' 'Tianji Yang' 'James Zou' 'Yongchan Kwon' 'Ruoxi Jia']"
]
|
null | null | 2405.03878 | null | null | http://arxiv.org/pdf/2405.03878v2 | 2024-06-04T05:28:56Z | 2024-05-06T21:49:29Z | Sequence Compression Speeds Up Credit Assignment in Reinforcement
Learning | Temporal credit assignment in reinforcement learning is challenging due to delayed and stochastic outcomes. Monte Carlo targets can bridge long delays between action and consequence but lead to high-variance targets due to stochasticity. Temporal difference (TD) learning uses bootstrapping to overcome variance but introduces a bias that can only be corrected through many iterations. TD($lambda$) provides a mechanism to navigate this bias-variance tradeoff smoothly. Appropriately selecting $lambda$ can significantly improve performance. Here, we propose Chunked-TD, which uses predicted probabilities of transitions from a model for computing $lambda$-return targets. Unlike other model-based solutions to credit assignment, Chunked-TD is less vulnerable to model inaccuracies. Our approach is motivated by the principle of history compression and 'chunks' trajectories for conventional TD learning. Chunking with learned world models compresses near-deterministic regions of the environment-policy interaction to speed up credit assignment while still bootstrapping when necessary. We propose algorithms that can be implemented online and show that they solve some problems much faster than conventional TD($lambda$). | [
"['Aditya A. Ramesh' 'Kenny Young' 'Louis Kirsch' 'Jürgen Schmidhuber']"
]
|
null | null | 2405.03879 | null | null | http://arxiv.org/pdf/2405.03879v1 | 2024-05-06T21:54:38Z | 2024-05-06T21:54:38Z | Scalable Amortized GPLVMs for Single Cell Transcriptomics Data | Dimensionality reduction is crucial for analyzing large-scale single-cell RNA-seq data. Gaussian Process Latent Variable Models (GPLVMs) offer an interpretable dimensionality reduction method, but current scalable models lack effectiveness in clustering cell types. We introduce an improved model, the amortized stochastic variational Bayesian GPLVM (BGPLVM), tailored for single-cell RNA-seq with specialized encoder, kernel, and likelihood designs. This model matches the performance of the leading single-cell variational inference (scVI) approach on synthetic and real-world COVID datasets and effectively incorporates cell-cycle and batch information to reveal more interpretable latent structures as we demonstrate on an innate immunity dataset. | [
"['Sarah Zhao' 'Aditya Ravuri' 'Vidhi Lalchand' 'Neil D. Lawrence']"
]
|
null | null | 2405.03880 | null | null | http://arxiv.org/pdf/2405.03880v1 | 2024-05-06T21:55:19Z | 2024-05-06T21:55:19Z | Efficient Radiation Treatment Planning based on Voxel Importance | Optimization is a time-consuming part of radiation treatment planning. We propose to reduce the optimization problem by only using a representative subset of informative voxels. This way, we improve planning efficiency while maintaining or enhancing the plan quality. To reduce the computational complexity of the optimization problem, we propose to subsample the set of voxels via importance sampling. We derive a sampling distribution based on an importance score that we obtain from pre-solving an easy optimization problem involving a simplified probing objective. By solving a reduced version of the original optimization problem using this subset, we effectively reduce the problem's size and computational demands while accounting for regions in which satisfactory dose deliveries are challenging. In contrast to other stochastic (sub-)sampling methods, our technique only requires a single sampling step to define a reduced optimization problem. This problem can be efficiently solved using established solvers. Empirical experiments on open benchmark data highlight substantially reduced optimization times, up to 50 times faster than the original ones, for intensity-modulated radiation therapy (IMRT), all while upholding plan quality comparable to traditional methods. Our approach has the potential to significantly accelerate radiation treatment planning by addressing its inherent computational challenges. We reduce the treatment planning time by reducing the size of the optimization problem rather than improving the optimization method. Our efforts are thus complementary to much of the previous developments. | [
"['Sebastian Mair' 'Anqi Fu' 'Jens Sjölund']"
]
|
null | null | 2405.03892 | null | null | http://arxiv.org/pdf/2405.03892v1 | 2024-05-06T22:44:32Z | 2024-05-06T22:44:32Z | Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning
via Causal Normalizing Flows | Despite notable successes of Reinforcement Learning (RL), the prevalent use of an online learning paradigm prevents its widespread adoption, especially in hazardous or costly scenarios. Offline RL has emerged as an alternative solution, learning from pre-collected static datasets. However, this offline learning introduces a new challenge known as distributional shift, degrading the performance when the policy is evaluated on scenarios that are Out-Of-Distribution (OOD) from the training dataset. Most existing offline RL resolves this issue by regularizing policy learning within the information supported by the given dataset. However, such regularization overlooks the potential for high-reward regions that may exist beyond the dataset. This motivates exploring novel offline learning techniques that can make improvements beyond the data support without compromising policy performance, potentially by learning causation (cause-and-effect) instead of correlation from the dataset. In this paper, we propose the MOOD-CRL (Model-based Offline OOD-Adapting Causal RL) algorithm, which aims to address the challenge of extrapolation for offline policy training through causal inference instead of policy-regularizing methods. Specifically, Causal Normalizing Flow (CNF) is developed to learn the transition and reward functions for data generation and augmentation in offline policy evaluation and training. Based on the data-invariant, physics-based qualitative causal graph and the observational data, we develop a novel learning scheme for CNF to learn the quantitative structural causal model. As a result, CNF gains predictive and counterfactual reasoning capabilities for sequential decision-making tasks, revealing a high potential for OOD adaptation. Our CNF-based offline RL approach is validated through empirical evaluations, outperforming model-free and model-based methods by a significant margin. | [
"['Minjae Cho' 'Jonathan P. How' 'Chuangchuang Sun']"
]
|
null | null | 2405.03894 | null | null | http://arxiv.org/pdf/2405.03894v2 | 2024-06-13T00:35:06Z | 2024-05-06T22:55:53Z | MVDiff: Scalable and Flexible Multi-View Diffusion for 3D Object
Reconstruction from Single-View | Generating consistent multiple views for 3D reconstruction tasks is still a challenge to existing image-to-3D diffusion models. Generally, incorporating 3D representations into diffusion model decrease the model's speed as well as generalizability and quality. This paper proposes a general framework to generate consistent multi-view images from single image or leveraging scene representation transformer and view-conditioned diffusion model. In the model, we introduce epipolar geometry constraints and multi-view attention to enforce 3D consistency. From as few as one image input, our model is able to generate 3D meshes surpassing baselines methods in evaluation metrics, including PSNR, SSIM and LPIPS. | [
"['Emmanuelle Bourigault' 'Pauline Bourigault']"
]
|
null | null | 2405.03904 | null | null | http://arxiv.org/pdf/2405.03904v1 | 2024-05-06T23:36:03Z | 2024-05-06T23:36:03Z | Transformer models classify random numbers | Random numbers are incredibly important in a variety of fields, and the need for their validation remains important. A Quantum Random Number Generator (QRNG) can theoretically generate truly random numbers however this does not remove the need to thoroughly test their randomness. Generally, the task of validating random numbers has been delegated to different statistical tests such as the tests from the NIST Statistical Test Suite (STS) which are often slow and only perform one task at a time. Our work presents a deep learning model that utilizes the transformer architecture to encode some of the tests from the NIST STS in a single model that also runs much faster. This model performs multi-label classification on these tests and outputs the probability of passing each statistical test that it encodes. We perform a thorough hyper-parameter optimization to converge on the best possible model and as a result, achieve a high degree of accuracy with a sample f1 score of above 0.9. | [
"['Rishabh Goel' 'YiZi Xiao' 'Ramin Ramezani']"
]
|
null | null | 2405.03911 | null | null | http://arxiv.org/pdf/2405.03911v1 | 2024-05-07T00:08:15Z | 2024-05-07T00:08:15Z | Federated Graph Condensation with Information Bottleneck Principles | Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediately benefited various graph learning tasks. However, existing graph condensation methods rely on centralized data storage, which is unfeasible for real-world decentralized data distribution, and overlook data holders' privacy-preserving requirements. To bridge the gap, we propose and study the novel problem of federated graph condensation for graph neural networks (GNNs). Specifically, we first propose a general framework for federated graph condensation, in which we decouple the typical gradient matching process for graph condensation into client-side gradient calculation and server-side gradient matching. In this way, the burdensome computation cost in client-side is largely alleviated. Besides, our empirical studies show that under the federated setting, the condensed graph will consistently leak data membership privacy, i.e., the condensed graph during the federated training can be utilized to steal the training data under the membership inference attacks (MIA). To tackle this issue, we innovatively incorporate information bottleneck principles into the federated graph condensation, which only needs to extract partial node features in one local pre-training step and utilize the features during federated training. Extensive experiments on real-world datasets demonstrate that our framework can consistently protect membership privacy during training. Meanwhile, it also achieves comparable and even superior performance against existing centralized graph condensation and federated graph learning methods. | [
"['Bo Yan']"
]
|
null | null | 2405.03913 | null | null | http://arxiv.org/pdf/2405.03913v2 | 2024-06-28T15:13:15Z | 2024-05-07T00:22:13Z | Digital Twin Calibration for Biological System-of-Systems: Cell Culture
Manufacturing Process | Biomanufacturing innovation relies on an efficient Design of Experiments (DoEs) to optimize processes and product quality. Traditional DoE methods, ignoring the underlying bioprocessing mechanisms, often suffer from a lack of interpretability and sample efficiency. This limitation motivates us to create a new optimal learning approach for digital twin model calibration. In this study, we consider the cell culture process multi-scale mechanistic model, also known as Biological System-of-Systems (Bio-SoS). This model with a modular design, composed of sub-models, allows us to integrate data across various production processes. To calibrate the Bio-SoS digital twin, we evaluate the mean squared error of model prediction and develop a computational approach to quantify the impact of parameter estimation error of individual sub-models on the prediction accuracy of digital twin, which can guide sample-efficient and interpretable DoEs. | [
"['Fuqiang Cheng' 'Wei Xie' 'Hua Zheng']"
]
|
null | null | 2405.03917 | null | null | http://arxiv.org/pdf/2405.03917v1 | 2024-05-07T00:25:20Z | 2024-05-07T00:25:20Z | KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference
with Coupled Quantization | Efficient deployment of Large Language Models (LLMs) requires batching multiple requests together to improve throughput. As the batch size, context length, or model size increases, the size of the key and value (KV) cache can quickly become the main contributor to GPU memory usage and the bottleneck of inference latency. Quantization has emerged as an effective technique for KV cache compression, but existing methods still fail at very low bit widths. We observe that distinct channels of a key/value activation embedding are highly inter-dependent, and the joint entropy of multiple channels grows at a slower rate than the sum of their marginal entropies. Based on this insight, we propose Coupled Quantization (CQ), which couples multiple key/value channels together to exploit their inter-dependency and encode the activations in a more information-efficient manner. Extensive experiments reveal that CQ outperforms or is competitive with existing baselines in preserving model quality. Furthermore, we demonstrate that CQ can preserve model quality with KV cache quantized down to 1-bit. | [
"['Tianyi Zhang' 'Jonah Yi' 'Zhaozhuo Xu' 'Anshumali Shrivastava']"
]
|
null | null | 2405.03918 | null | null | http://arxiv.org/pdf/2405.03918v1 | 2024-05-07T00:36:56Z | 2024-05-07T00:36:56Z | Unlearning Backdoor Attacks through Gradient-Based Model Pruning | In the era of increasing concerns over cybersecurity threats, defending against backdoor attacks is paramount in ensuring the integrity and reliability of machine learning models. However, many existing approaches require substantial amounts of data for effective mitigation, posing significant challenges in practical deployment. To address this, we propose a novel approach to counter backdoor attacks by treating their mitigation as an unlearning task. We tackle this challenge through a targeted model pruning strategy, leveraging unlearning loss gradients to identify and eliminate backdoor elements within the model. Built on solid theoretical insights, our approach offers simplicity and effectiveness, rendering it well-suited for scenarios with limited data availability. Our methodology includes formulating a suitable unlearning loss and devising a model-pruning technique tailored for convolutional neural networks. Comprehensive evaluations demonstrate the efficacy of our proposed approach compared to state-of-the-art approaches, particularly in realistic data settings. | [
"['Kealan Dunnett' 'Reza Arablouei' 'Dimity Miller' 'Volkan Dedeoglu'\n 'Raja Jurdak']"
]
|
null | null | 2405.03924 | null | null | http://arxiv.org/pdf/2405.03924v2 | 2024-07-04T08:48:45Z | 2024-05-07T00:51:48Z | NeurDB: An AI-powered Autonomous Data System | In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan. | [
"['Beng Chin Ooi' 'Shaofeng Cai' 'Gang Chen' 'Yanyan Shen' 'Kian-Lee Tan'\n 'Yuncheng Wu' 'Xiaokui Xiao' 'Naili Xing' 'Cong Yue' 'Lingze Zeng'\n 'Meihui Zhang' 'Zhanhao Zhao']"
]
|
null | null | 2405.03942 | null | null | http://arxiv.org/pdf/2405.03942v1 | 2024-05-07T02:03:07Z | 2024-05-07T02:03:07Z | Collaborative Intelligence in Sequential Experiments: A
Human-in-the-Loop Framework for Drug Discovery | Drug discovery is a complex process that involves sequentially screening and examining a vast array of molecules to identify those with the target properties. This process, also referred to as sequential experimentation, faces challenges due to the vast search space, the rarity of target molecules, and constraints imposed by limited data and experimental budgets. To address these challenges, we introduce a human-in-the-loop framework for sequential experiments in drug discovery. This collaborative approach combines human expert knowledge with deep learning algorithms, enhancing the discovery of target molecules within a specified experimental budget. The proposed algorithm processes experimental data to recommend both promising molecules and those that could improve its performance to human experts. Human experts retain the final decision-making authority based on these recommendations and their domain expertise, including the ability to override algorithmic recommendations. We applied our method to drug discovery tasks using real-world data and found that it consistently outperforms all baseline methods, including those which rely solely on human or algorithmic input. This demonstrates the complementarity between human experts and the algorithm. Our results provide key insights into the levels of humans' domain knowledge, the importance of meta-knowledge, and effective work delegation strategies. Our findings suggest that such a framework can significantly accelerate the development of new vaccines and drugs by leveraging the best of both human and artificial intelligence. | [
"['Jinghai He' 'Cheng Hua' 'Yingfei Wang' 'Zeyu Zheng']"
]
|
null | null | 2405.03943 | null | null | http://arxiv.org/pdf/2405.03943v1 | 2024-05-07T02:05:30Z | 2024-05-07T02:05:30Z | Predictive Modeling with Temporal Graphical Representation on Electronic
Health Records | Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships between historical visits and medical events, and the inherent structural information within these elements. Existing patient representation methods can be roughly categorized into sequential representation and graphical representation. The sequential representation methods focus only on the temporal relationships among longitudinal visits. On the other hand, the graphical representation approaches, while adept at extracting the graph-structured relationships between various medical events, fall short in effectively integrate temporal information. To capture both types of information, we model a patient's EHR as a novel temporal heterogeneous graph. This graph includes historical visits nodes and medical events nodes. It propagates structured information from medical event nodes to visit nodes and utilizes time-aware visit nodes to capture changes in the patient's health status. Furthermore, we introduce a novel temporal graph transformer (TRANS) that integrates temporal edge features, global positional encoding, and local structural encoding into heterogeneous graph convolution, capturing both temporal and structural information. We validate the effectiveness of TRANS through extensive experiments on three real-world datasets. The results show that our proposed approach achieves state-of-the-art performance. | [
"['Jiayuan Chen' 'Changchang Yin' 'Yuanlong Wang' 'Ping Zhang']"
]
|
null | null | 2405.03949 | null | null | http://arxiv.org/pdf/2405.03949v1 | 2024-05-07T02:12:38Z | 2024-05-07T02:12:38Z | FedSC: Provable Federated Self-supervised Learning with Spectral
Contrastive Objective over Non-i.i.d. Data | Recent efforts have been made to integrate self-supervised learning (SSL) with the framework of federated learning (FL). One unique challenge of federated self-supervised learning (FedSSL) is that the global objective of FedSSL usually does not equal the weighted sum of local SSL objectives. Consequently, conventional approaches, such as federated averaging (FedAvg), fail to precisely minimize the FedSSL global objective, often resulting in suboptimal performance, especially when data is non-i.i.d.. To fill this gap, we propose a provable FedSSL algorithm, named FedSC, based on the spectral contrastive objective. In FedSC, clients share correlation matrices of data representations in addition to model weights periodically, which enables inter-client contrast of data samples in addition to intra-client contrast and contraction, resulting in improved quality of data representations. Differential privacy (DP) protection is deployed to control the additional privacy leakage on local datasets when correlation matrices are shared. We also provide theoretical analysis on the convergence and extra privacy leakage. The experimental results validate the effectiveness of our proposed algorithm. | [
"['Shusen Jing' 'Anlan Yu' 'Shuai Zhang' 'Songyang Zhang']"
]
|
null | null | 2405.03950 | null | null | http://arxiv.org/pdf/2405.03950v1 | 2024-05-07T02:16:54Z | 2024-05-07T02:16:54Z | Relating-Up: Advancing Graph Neural Networks through Inter-Graph
Relationships | Graph Neural Networks (GNNs) have excelled in learning from graph-structured data, especially in understanding the relationships within a single graph, i.e., intra-graph relationships. Despite their successes, GNNs are limited by neglecting the context of relationships across graphs, i.e., inter-graph relationships. Recognizing the potential to extend this capability, we introduce Relating-Up, a plug-and-play module that enhances GNNs by exploiting inter-graph relationships. This module incorporates a relation-aware encoder and a feedback training strategy. The former enables GNNs to capture relationships across graphs, enriching relation-aware graph representation through collective context. The latter utilizes a feedback loop mechanism for the recursively refinement of these representations, leveraging insights from refining inter-graph dynamics to conduct feedback loop. The synergy between these two innovations results in a robust and versatile module. Relating-Up enhances the expressiveness of GNNs, enabling them to encapsulate a wider spectrum of graph relationships with greater precision. Our evaluations across 16 benchmark datasets demonstrate that integrating Relating-Up into GNN architectures substantially improves performance, positioning Relating-Up as a formidable choice for a broad spectrum of graph representation learning tasks. | [
"['Qi Zou' 'Na Yu' 'Daoliang Zhang' 'Wei Zhang' 'Rui Gao']"
]
|
null | null | 2405.03955 | null | null | http://arxiv.org/abs/2405.03955v1 | 2024-05-07T02:29:41Z | 2024-05-07T02:29:41Z | IPFed: Identity protected federated learning for user authentication | With the development of laws and regulations related to privacy preservation, it has become difficult to collect personal data to perform machine learning. In this context, federated learning, which is distributed learning without sharing personal data, has been proposed. In this paper, we focus on federated learning for user authentication. We show that it is difficult to achieve both privacy preservation and high accuracy with existing methods. To address these challenges, we propose IPFed which is privacy-preserving federated learning using random projection for class embedding. Furthermore, we prove that IPFed is capable of learning equivalent to the state-of-the-art method. Experiments on face image datasets show that IPFed can protect the privacy of personal data while maintaining the accuracy of the state-of-the-art method. | [
"['Yosuke Kaga' 'Yusei Suzuki' 'Kenta Takahashi']"
]
|
null | null | 2405.03958 | null | null | http://arxiv.org/pdf/2405.03958v1 | 2024-05-07T02:45:28Z | 2024-05-07T02:45:28Z | Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your
Diffusion Model | Current state-of-the-art diffusion models employ U-Net architectures containing convolutional and (qkv) self-attention layers. The U-Net processes images while being conditioned on the time embedding input for each sampling step and the class or caption embedding input corresponding to the desired conditional generation. Such conditioning involves scale-and-shift operations to the convolutional layers but does not directly affect the attention layers. While these standard architectural choices are certainly effective, not conditioning the attention layers feels arbitrary and potentially suboptimal. In this work, we show that simply adding LoRA conditioning to the attention layers without changing or tuning the other parts of the U-Net architecture improves the image generation quality. For example, a drop-in addition of LoRA conditioning to EDM diffusion model yields FID scores of 1.91/1.75 for unconditional and class-conditional CIFAR-10 generation, improving upon the baseline of 1.97/1.79. | [
"['Joo Young Choi' 'Jaesung R. Park' 'Inkyu Park' 'Jaewoong Cho'\n 'Albert No' 'Ernest K. Ryu']"
]
|
null | null | 2405.03961 | null | null | http://arxiv.org/pdf/2405.03961v2 | 2024-07-02T13:28:28Z | 2024-05-07T02:48:15Z | Structure-based drug design by denoising voxel grids | We present VoxBind, a new score-based generative model for 3D molecules conditioned on protein structures. Our approach represents molecules as 3D atomic density grids and leverages a 3D voxel-denoising network for learning and generation. We extend the neural empirical Bayes formalism (Saremi & Hyvarinen, 2019) to the conditional setting and generate structure-conditioned molecules with a two-step procedure: (i) sample noisy molecules from the Gaussian-smoothed conditional distribution with underdamped Langevin MCMC using the learned score function and (ii) estimate clean molecules from the noisy samples with single-step denoising. Compared to the current state of the art, our model is simpler to train, significantly faster to sample from, and achieves better results on extensive in silico benchmarks -- the generated molecules are more diverse, exhibit fewer steric clashes, and bind with higher affinity to protein pockets. The code is available at https://github.com/genentech/voxbind/. | [
"['Pedro O. Pinheiro' 'Arian Jamasb' 'Omar Mahmood' 'Vishnu Sresht'\n 'Saeed Saremi']"
]
|
null | null | 2405.03962 | null | null | http://arxiv.org/pdf/2405.03962v1 | 2024-05-07T02:49:21Z | 2024-05-07T02:49:21Z | AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion | Determining the optimal configuration of adsorbates on a slab (adslab) is pivotal in the exploration of novel catalysts across diverse applications. Traditionally, the quest for the lowest energy adslab configuration involves placing the adsorbate onto the slab followed by an optimization process. Prior methodologies have relied on heuristics, problem-specific intuitions, or brute-force approaches to guide adsorbate placement. In this work, we propose a novel framework for adsorbate placement using denoising diffusion. The model is designed to predict the optimal adsorbate site and orientation corresponding to the lowest energy configuration. Further, we have an end-to-end evaluation framework where diffusion-predicted adslab configuration is optimized with a pretrained machine learning force field and finally evaluated with Density Functional Theory (DFT). Our findings demonstrate an acceleration of up to 5x or 3.5x improvement in accuracy compared to the previous best approach. Given the novelty of this framework and application, we provide insights into the impact of pre-training, model architectures, and conduct extensive experiments to underscore the significance of this approach. | [
"['Adeesh Kolluru' 'John R Kitchin']"
]
|
null | null | 2405.03963 | null | null | http://arxiv.org/pdf/2405.03963v2 | 2024-05-14T15:43:11Z | 2024-05-07T02:49:59Z | ERATTA: Extreme RAG for Table To Answers with Large Language Models | Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions in the recent past. However, the choice of use-cases that incorporate RAG with LLMs have been either generic or extremely domain specific, thereby questioning the scalability and generalizability of RAG-LLM approaches. In this work, we propose a unique LLM-based system where multiple LLMs can be invoked to enable data authentication, user query routing, data retrieval and custom prompting for question answering capabilities from data tables that are highly varying and large in size. Our system is tuned to extract information from Enterprise-level data products and furnish real time responses under 10 seconds. One prompt manages user-to-data authentication followed by three prompts to route, fetch data and generate a customizable prompt natural language responses. Additionally, we propose a five metric scoring module that detects and reports hallucinations in the LLM responses. Our proposed system and scoring metrics achieve >90% confidence scores across hundreds of user queries in the sustainability, financial health and social media domains. Extensions to the proposed extreme RAG architectures can enable heterogeneous source querying using LLMs. | [
"['Sohini Roychowdhury' 'Marko Krema' 'Anvar Mahammad' 'Brian Moore'\n 'Arijit Mukherjee' 'Punit Prakashchandra']"
]
|
null | null | 2405.03967 | null | null | http://arxiv.org/pdf/2405.03967v1 | 2024-05-07T02:54:31Z | 2024-05-07T02:54:31Z | SwiftRL: Towards Efficient Reinforcement Learning on Real
Processing-In-Memory Systems | Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward signals from experience datasets. However, RL training often faces memory limitations, leading to execution latencies and prolonged training times. To overcome this, SwiftRL explores Processing-In-Memory (PIM) architectures to accelerate RL workloads. We achieve near-linear performance scaling by implementing RL algorithms like Tabular Q-learning and SARSA on UPMEM PIM systems and optimizing for hardware. Our experiments on OpenAI GYM environments using UPMEM hardware demonstrate superior performance compared to CPU and GPU implementations. | [
"['Kailash Gogineni' 'Sai Santosh Dayapule' 'Juan Gómez-Luna'\n 'Karthikeya Gogineni' 'Peng Wei' 'Tian Lan' 'Mohammad Sadrosadati'\n 'Onur Mutlu' 'Guru Venkataramani']"
]
|
null | null | 2405.03974 | null | null | http://arxiv.org/pdf/2405.03974v1 | 2024-05-07T03:08:30Z | 2024-05-07T03:08:30Z | TBNet: A Neural Architectural Defense Framework Facilitating DNN Model
Protection in Trusted Execution Environments | Trusted Execution Environments (TEEs) have become a promising solution to secure DNN models on edge devices. However, the existing solutions either provide inadequate protection or introduce large performance overhead. Taking both security and performance into consideration, this paper presents TBNet, a TEE-based defense framework that protects DNN model from a neural architectural perspective. Specifically, TBNet generates a novel Two-Branch substitution model, to respectively exploit (1) the computational resources in the untrusted Rich Execution Environment (REE) for latency reduction and (2) the physically-isolated TEE for model protection. Experimental results on a Raspberry Pi across diverse DNN model architectures and datasets demonstrate that TBNet achieves efficient model protection at a low cost. | [
"['Ziyu Liu' 'Tong Zhou' 'Yukui Luo' 'Xiaolin Xu']"
]
|
null | null | 2405.03977 | null | null | http://arxiv.org/abs/2405.03977v1 | 2024-05-07T03:29:11Z | 2024-05-07T03:29:11Z | Can citations tell us about a paper's reproducibility? A case study of
machine learning papers | The iterative character of work in machine learning (ML) and artificial intelligence (AI) and reliance on comparisons against benchmark datasets emphasize the importance of reproducibility in that literature. Yet, resource constraints and inadequate documentation can make running replications particularly challenging. Our work explores the potential of using downstream citation contexts as a signal of reproducibility. We introduce a sentiment analysis framework applied to citation contexts from papers involved in Machine Learning Reproducibility Challenges in order to interpret the positive or negative outcomes of reproduction attempts. Our contributions include training classifiers for reproducibility-related contexts and sentiment analysis, and exploring correlations between citation context sentiment and reproducibility scores. Study data, software, and an artifact appendix are publicly available at https://github.com/lamps-lab/ccair-ai-reproducibility . | [
"['Rochana R. Obadage' 'Sarah M. Rajtmajer' 'Jian Wu']"
]
|
null | null | 2405.03981 | null | null | http://arxiv.org/pdf/2405.03981v1 | 2024-05-07T03:42:49Z | 2024-05-07T03:42:49Z | Predicting Lung Disease Severity via Image-Based AQI Analysis using Deep
Learning Techniques | Air pollution is a significant health concern worldwide, contributing to various respiratory diseases. Advances in air quality mapping, driven by the emergence of smart cities and the proliferation of Internet-of-Things sensor devices, have led to an increase in available data, fueling momentum in air pollution forecasting. The objective of this study is to devise an integrated approach for predicting air quality using image data and subsequently assessing lung disease severity based on Air Quality Index (AQI).The aim is to implement an integrated approach by refining existing techniques to improve accuracy in predicting AQI and lung disease severity. The study aims to forecast additional atmospheric pollutants like AQI, PM10, O3, CO, SO2, NO2 in addition to PM2.5 levels. Additionally, the study aims to compare the proposed approach with existing methods to show its effectiveness. The approach used in this paper uses VGG16 model for feature extraction in images and neural network for predicting AQI.In predicting lung disease severity, Support Vector Classifier (SVC) and K-Nearest Neighbors (KNN) algorithms are utilized. The neural network model for predicting AQI achieved training accuracy of 88.54 % and testing accuracy of 87.44%,which was measured using loss function, while the KNN model used for predicting lung disease severity achieved training accuracy of 98.4% and testing accuracy of 97.5% In conclusion, the integrated approach presented in this study forecasts air quality and evaluates lung disease severity, achieving high testing accuracies of 87.44% for AQI and 97.5% for lung disease severity using neural network, KNN, and SVC models. The future scope involves implementing transfer learning and advanced deep learning modules to enhance prediction capabilities. While the current study focuses on India, the objective is to expand its scope to encompass global coverage. | [
"['Anvita Mahajan' 'Sayali Mate' 'Chinmayee Kulkarni' 'Suraj Sawant']"
]
|
null | null | 2405.03987 | null | null | http://arxiv.org/pdf/2405.03987v2 | 2024-05-08T01:34:25Z | 2024-05-07T03:55:57Z | Navigating Chemical Space with Latent Flows | Recent progress of deep generative models in the vision and language domain has stimulated significant interest in more structured data generation such as molecules. However, beyond generating new random molecules, efficient exploration and a comprehensive understanding of the vast chemical space are of great importance to molecular science and applications in drug design and materials discovery. In this paper, we propose a new framework, ChemFlow, to traverse chemical space through navigating the latent space learned by molecule generative models through flows. We introduce a dynamical system perspective that formulates the problem as learning a vector field that transports the mass of the molecular distribution to the region with desired molecular properties or structure diversity. Under this framework, we unify previous approaches on molecule latent space traversal and optimization and propose alternative competing methods incorporating different physical priors. We validate the efficacy of ChemFlow on molecule manipulation and single- and multi-objective molecule optimization tasks under both supervised and unsupervised molecular discovery settings. Codes and demos are publicly available on GitHub at https://github.com/garywei944/ChemFlow. | [
"['Guanghao Wei' 'Yining Huang' 'Chenru Duan' 'Yue Song' 'Yuanqi Du']"
]
|
null | null | 2405.03991 | null | null | http://arxiv.org/pdf/2405.03991v1 | 2024-05-07T04:10:01Z | 2024-05-07T04:10:01Z | Assemblage: Automatic Binary Dataset Construction for Machine Learning | Binary code is pervasive, and binary analysis is a key task in reverse engineering, malware classification, and vulnerability discovery. Unfortunately, while there exist large corpuses of malicious binaries, obtaining high-quality corpuses of benign binaries for modern systems has proven challenging (e.g., due to licensing issues). Consequently, machine learning based pipelines for binary analysis utilize either costly commercial corpuses (e.g., VirusTotal) or open-source binaries (e.g., coreutils) available in limited quantities. To address these issues, we present Assemblage: an extensible cloud-based distributed system that crawls, configures, and builds Windows PE binaries to obtain high-quality binary corpuses suitable for training state-of-the-art models in binary analysis. We have run Assemblage on AWS over the past year, producing 890k Windows PE and 428k Linux ELF binaries across 29 configurations. Assemblage is designed to be both reproducible and extensible, enabling users to publish "recipes" for their datasets, and facilitating the extraction of a wide array of features. We evaluated Assemblage by using its data to train modern learning-based pipelines for compiler provenance and binary function similarity. Our results illustrate the practical need for robust corpuses of high-quality Windows PE binaries in training modern learning-based binary analyses. Assemblage can be downloaded from https://assemblage-dataset.net | [
"['Chang Liu' 'Rebecca Saul' 'Yihao Sun' 'Edward Raff' 'Maya Fuchs'\n 'Townsend Southard Pantano' 'James Holt' 'Kristopher Micinski']"
]
|
null | null | 2405.04003 | null | null | http://arxiv.org/pdf/2405.04003v1 | 2024-05-07T04:44:59Z | 2024-05-07T04:44:59Z | High Energy Density Radiative Transfer in the Diffusion Regime with
Fourier Neural Operators | Radiative heat transfer is a fundamental process in high energy density physics and inertial fusion. Accurately predicting the behavior of Marshak waves across a wide range of material properties and drive conditions is crucial for design and analysis of these systems. Conventional numerical solvers and analytical approximations often face challenges in terms of accuracy and computational efficiency. In this work, we propose a novel approach to model Marshak waves using Fourier Neural Operators (FNO). We develop two FNO-based models: (1) a base model that learns the mapping between the drive condition and material properties to a solution approximation based on the widely used analytic model by Hammer & Rosen (2003), and (2) a model that corrects the inaccuracies of the analytic approximation by learning the mapping to a more accurate numerical solution. Our results demonstrate the strong generalization capabilities of the FNOs and show significant improvements in prediction accuracy compared to the base analytic model. | [
"['Joseph Farmer' 'Ethan Smith' 'William Bennett' 'Ryan McClarren']"
]
|
null | null | 2405.04017 | null | null | http://arxiv.org/pdf/2405.04017v1 | 2024-05-07T05:29:55Z | 2024-05-07T05:29:55Z | An Improved Finite-time Analysis of Temporal Difference Learning with
Deep Neural Networks | Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical understanding of these algorithms remains challenging due to the nonlinearity of the action-value approximation. In this paper, we develop an improved non-asymptotic analysis of the neural TD method with a general $L$-layer neural network. New proof techniques are developed and an improved new $tilde{mathcal{O}}(epsilon^{-1})$ sample complexity is derived. To our best knowledge, this is the first finite-time analysis of neural TD that achieves an $tilde{mathcal{O}}(epsilon^{-1})$ complexity under the Markovian sampling, as opposed to the best known $tilde{mathcal{O}}(epsilon^{-2})$ complexity in the existing literature. | [
"['Zhifa Ke' 'Zaiwen Wen' 'Junyu Zhang']"
]
|
null | null | 2405.04025 | null | null | http://arxiv.org/pdf/2405.04025v1 | 2024-05-07T05:58:44Z | 2024-05-07T05:58:44Z | Optimal Group Fair Classifiers from Linear Post-Processing | We propose a post-processing algorithm for fair classification that mitigates model bias under a unified family of group fairness criteria covering statistical parity, equal opportunity, and equalized odds, applicable to multi-class problems and both attribute-aware and attribute-blind settings. It achieves fairness by re-calibrating the output score of the given base model with a "fairness cost" -- a linear combination of the (predicted) group memberships. Our algorithm is based on a representation result showing that the optimal fair classifier can be expressed as a linear post-processing of the loss function and the group predictor, derived via using these as sufficient statistics to reformulate the fair classification problem as a linear program. The parameters of the post-processor are estimated by solving the empirical LP. Experiments on benchmark datasets show the efficiency and effectiveness of our algorithm at reducing disparity compared to existing algorithms, including in-processing, especially on larger problems. | [
"['Ruicheng Xian' 'Han Zhao']"
]
|
null | null | 2405.04026 | null | null | http://arxiv.org/pdf/2405.04026v1 | 2024-05-07T05:59:10Z | 2024-05-07T05:59:10Z | Federated Control in Markov Decision Processes | We study problems of federated control in Markov Decision Processes. To solve an MDP with large state space, multiple learning agents are introduced to collaboratively learn its optimal policy without communication of locally collected experience. In our settings, these agents have limited capabilities, which means they are restricted within different regions of the overall state space during the training process. In face of the difference among restricted regions, we firstly introduce concepts of leakage probabilities to understand how such heterogeneity affects the learning process, and then propose a novel communication protocol that we call Federated-Q protocol (FedQ), which periodically aggregates agents' knowledge of their restricted regions and accordingly modifies their learning problems for further training. In terms of theoretical analysis, we justify the correctness of FedQ as a communication protocol, then give a general result on sample complexity of derived algorithms FedQ-X with the RL oracle , and finally conduct a thorough study on the sample complexity of FedQ-SynQ. Specifically, FedQ-X has been shown to enjoy linear speedup in terms of sample complexity when workload is uniformly distributed among agents. Moreover, we carry out experiments in various environments to justify the efficiency of our methods. | [
"['Hao Jin' 'Yang Peng' 'Liangyu Zhang' 'Zhihua Zhang']"
]
|
null | null | 2405.04034 | null | null | http://arxiv.org/pdf/2405.04034v1 | 2024-05-07T06:09:37Z | 2024-05-07T06:09:37Z | Differentially Private Post-Processing for Fair Regression | This paper describes a differentially private post-processing algorithm for learning fair regressors satisfying statistical parity, addressing privacy concerns of machine learning models trained on sensitive data, as well as fairness concerns of their potential to propagate historical biases. Our algorithm can be applied to post-process any given regressor to improve fairness by remapping its outputs. It consists of three steps: first, the output distributions are estimated privately via histogram density estimation and the Laplace mechanism, then their Wasserstein barycenter is computed, and the optimal transports to the barycenter are used for post-processing to satisfy fairness. We analyze the sample complexity of our algorithm and provide fairness guarantee, revealing a trade-off between the statistical bias and variance induced from the choice of the number of bins in the histogram, in which using less bins always favors fairness at the expense of error. | [
"['Ruicheng Xian' 'Qiaobo Li' 'Gautam Kamath' 'Han Zhao']"
]
|
null | null | 2405.04039 | null | null | http://arxiv.org/pdf/2405.04039v1 | 2024-05-07T06:23:02Z | 2024-05-07T06:23:02Z | Utilizing GPT to Enhance Text Summarization: A Strategy to Minimize
Hallucinations | In this research, we uses the DistilBERT model to generate extractive summary and the T5 model to generate abstractive summaries. Also, we generate hybrid summaries by combining both DistilBERT and T5 models. Central to our research is the implementation of GPT-based refining process to minimize the common problem of hallucinations that happens in AI-generated summaries. We evaluate unrefined summaries and, after refining, we also assess refined summaries using a range of traditional and novel metrics, demonstrating marked improvements in the accuracy and reliability of the summaries. Results highlight significant improvements in reducing hallucinatory content, thereby increasing the factual integrity of the summaries. | [
"['Hassan Shakil' 'Zeydy Ortiz' 'Grant C. Forbes']"
]
|
null | null | 2405.04043 | null | null | http://arxiv.org/pdf/2405.04043v1 | 2024-05-07T06:29:06Z | 2024-05-07T06:29:06Z | Scalable Vertical Federated Learning via Data Augmentation and Amortized
Inference | Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates. This paper introduces the first comprehensive framework for fitting Bayesian models in the VFL setting. We propose a novel approach that leverages data augmentation techniques to transform VFL problems into a form compatible with existing Bayesian federated learning algorithms. We present an innovative model formulation for specific VFL scenarios where the joint likelihood factorizes into a product of client-specific likelihoods. To mitigate the dimensionality challenge posed by data augmentation, which scales with the number of observations and clients, we develop a factorized amortized variational approximation that achieves scalability independent of the number of observations. We showcase the efficacy of our framework through extensive numerical experiments on logistic regression, multilevel regression, and a novel hierarchical Bayesian split neural net model. Our work paves the way for privacy-preserving, decentralized Bayesian inference in vertically partitioned data scenarios, opening up new avenues for research and applications in various domains. | [
"['Conor Hassan' 'Matthew Sutton' 'Antonietta Mira' 'Kerrie Mengersen']"
]
|
null | null | 2405.04049 | null | null | http://arxiv.org/pdf/2405.04049v1 | 2024-05-07T06:42:30Z | 2024-05-07T06:42:30Z | Watermarking Neuromorphic Brains: Intellectual Property Protection in
Spiking Neural Networks | As spiking neural networks (SNNs) gain traction in deploying neuromorphic computing solutions, protecting their intellectual property (IP) has become crucial. Without adequate safeguards, proprietary SNN architectures are at risk of theft, replication, or misuse, which could lead to significant financial losses for the owners. While IP protection techniques have been extensively explored for artificial neural networks (ANNs), their applicability and effectiveness for the unique characteristics of SNNs remain largely unexplored. In this work, we pioneer an investigation into adapting two prominent watermarking approaches, namely, fingerprint-based and backdoor-based mechanisms to secure proprietary SNN architectures. We conduct thorough experiments to evaluate the impact on fidelity, resilience against overwrite threats, and resistance to compression attacks when applying these watermarking techniques to SNNs, drawing comparisons with their ANN counterparts. This study lays the groundwork for developing neuromorphic-aware IP protection strategies tailored to the distinctive dynamics of SNNs. | [
"['Hamed Poursiami' 'Ihsen Alouani' 'Maryam Parsa']"
]
|
null | null | 2405.04053 | null | null | http://arxiv.org/pdf/2405.04053v1 | 2024-05-07T06:52:34Z | 2024-05-07T06:52:34Z | Evaluating Text Summaries Generated by Large Language Models Using
OpenAI's GPT | This research examines the effectiveness of OpenAI's GPT models as independent evaluators of text summaries generated by six transformer-based models from Hugging Face: DistilBART, BERT, ProphetNet, T5, BART, and PEGASUS. We evaluated these summaries based on essential properties of high-quality summary - conciseness, relevance, coherence, and readability - using traditional metrics such as ROUGE and Latent Semantic Analysis (LSA). Uniquely, we also employed GPT not as a summarizer but as an evaluator, allowing it to independently assess summary quality without predefined metrics. Our analysis revealed significant correlations between GPT evaluations and traditional metrics, particularly in assessing relevance and coherence. The results demonstrate GPT's potential as a robust tool for evaluating text summaries, offering insights that complement established metrics and providing a basis for comparative analysis of transformer-based models in natural language processing tasks. | [
"['Hassan Shakil' 'Atqiya Munawara Mahi' 'Phuoc Nguyen' 'Zeydy Ortiz'\n 'Mamoun T. Mardini']"
]
|
null | null | 2405.04056 | null | null | http://arxiv.org/pdf/2405.04056v1 | 2024-05-07T06:57:42Z | 2024-05-07T06:57:42Z | Bidirectional Adversarial Autoencoders for the design of Plasmonic
Metasurfaces | Deep Learning has been a critical part of designing inverse design methods that are computationally efficient and accurate. An example of this is the design of photonic metasurfaces by using their photoluminescent spectrum as the input data to predict their topology. One fundamental challenge of these systems is their ability to represent nonlinear relationships between sets of data that have different dimensionalities. Existing design methods often implement a conditional Generative Adversarial Network in order to solve this problem, but in many cases the solution is unable to generate structures that provide multiple peaks when validated. It is demonstrated that in response to the target spectrum, the Bidirectional Adversarial Autoencoder is able to generate structures that provide multiple peaks on several occasions. As a result the proposed model represents an important advance towards the generation of nonlinear photonic metasurfaces that can be used in advanced metasurface design. | [
"['Yuansan Liu' 'Jeygopi Panisilvam' 'Peter Dower' 'Sejeong Kim'\n 'James Bailey']"
]
|
null | null | 2405.04061 | null | null | http://arxiv.org/pdf/2405.04061v3 | 2024-06-06T02:02:00Z | 2024-05-07T07:07:44Z | Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications | Divergence measures play a central role and become increasingly essential in deep learning, yet efficient measures for multiple (more than two) distributions are rarely explored. This becomes particularly crucial in areas where the simultaneous management of multiple distributions is both inevitable and essential. Examples include clustering, multi-source domain adaptation or generalization, and multi-view learning, among others. While computing the mean of pairwise distances between any two distributions is a prevalent method to quantify the total divergence among multiple distributions, it is imperative to acknowledge that this approach is not straightforward and necessitates significant computational resources. In this study, we introduce a new divergence measure tailored for multiple distributions named the generalized Cauchy-Schwarz divergence (GCSD). Additionally, we furnish a kernel-based closed-form sample estimator, making it convenient and straightforward to use in various machine-learning applications. Finally, we explore its profound implications in the realm of deep learning by applying it to tackle two thoughtfully chosen machine-learning tasks: deep clustering and multi-source domain adaptation. Our extensive experimental investigations confirm the robustness and effectiveness of GCSD in both scenarios. The findings also underscore the innovative potential of GCSD and its capability to significantly propel machine learning methodologies that necessitate the quantification of multiple distributions. | [
"['Mingfei Lu' 'Chenxu Li' 'Shujian Yu' 'Robert Jenssen' 'Badong Chen']"
]
|
null | null | 2405.04074 | null | null | http://arxiv.org/pdf/2405.04074v1 | 2024-05-07T07:20:15Z | 2024-05-07T07:20:15Z | A simple theory for training response of deep neural networks | Deep neural networks give us a powerful method to model the training dataset's relationship between input and output. We can regard that as a complex adaptive system consisting of many artificial neurons that work as an adaptive memory as a whole. The network's behavior is training dynamics with a feedback loop from the evaluation of the loss function. We already know the training response can be constant or shows power law-like aging in some ideal situations. However, we still have gaps between those findings and other complex phenomena, like network fragility. To fill the gap, we introduce a very simple network and analyze it. We show the training response consists of some different factors based on training stages, activation functions, or training methods. In addition, we show feature space reduction as an effect of stochastic training dynamics, which can result in network fragility. Finally, we discuss some complex phenomena of deep networks. | [
"['Kenichi Nakazato']"
]
|
null | null | 2405.04078 | null | null | http://arxiv.org/pdf/2405.04078v1 | 2024-05-07T07:21:20Z | 2024-05-07T07:21:20Z | WISER: Weak supervISion and supErvised Representation learning to
improve drug response prediction in cancer | Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers (`cell lines') is experimentally determined in laboratory settings. Nevertheless, variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Moreover, while genomic profiles of many cancer patients are readily available, the scarcity of corresponding drug response data limits the ability to train machine learning models that can predict drug response in patients effectively. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning followed by a downstream drug response classification step. Introducing supervision in both stages is challenging due to heterogeneous patient response to drugs and limited drug response data. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second. Experimental results on real patient data demonstrate the efficacy of our method (WISER) over state-of-the-art alternatives on predicting personalized drug response. | [
"['Kumar Shubham' 'Aishwarya Jayagopal' 'Syed Mohammed Danish'\n 'Prathosh AP' 'Vaibhav Rajan']"
]
|
null | null | 2405.04097 | null | null | http://arxiv.org/pdf/2405.04097v1 | 2024-05-07T07:57:15Z | 2024-05-07T07:57:15Z | Unmasking Illusions: Understanding Human Perception of Audiovisual
Deepfakes | The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain; therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-ofthe-art audiovisual deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a webbased platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. | [
"['Ammarah Hashmi' 'Sahibzada Adil Shahzad' 'Chia-Wen Lin' 'Yu Tsao'\n 'Hsin-Min Wang']"
]
|
null | null | 2405.04098 | null | null | http://arxiv.org/pdf/2405.04098v1 | 2024-05-07T08:05:20Z | 2024-05-07T08:05:20Z | Binarized Simplicial Convolutional Neural Networks | Graph Neural Networks have a limitation of solely processing features on graph nodes, neglecting data on high-dimensional structures such as edges and triangles. Simplicial Convolutional Neural Networks (SCNN) represent higher-order structures using simplicial complexes to break this limitation albeit still lacking time efficiency. In this paper, we propose a novel neural network architecture on simplicial complexes named Binarized Simplicial Convolutional Neural Networks (Bi-SCNN) based on the combination of simplicial convolution with a binary-sign forward propagation strategy. The usage of the Hodge Laplacian on a binary-sign forward propagation enables Bi-SCNN to efficiently and effectively represent simplicial features that have higher-order structures than traditional graph node representations. Compared to the previous Simplicial Convolutional Neural Networks, the reduced model complexity of Bi-SCNN shortens the execution time without sacrificing the prediction performance and is less prone to the over-smoothing effect. Experimenting with real-world citation and ocean-drifter data confirmed that our proposed Bi-SCNN is efficient and accurate. | [
"['Yi Yan' 'Ercan E. Kuruoglu']"
]
|
null | null | 2405.04100 | null | null | http://arxiv.org/pdf/2405.04100v1 | 2024-05-07T08:15:37Z | 2024-05-07T08:15:37Z | ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in
Emergency Scenarios | Emergent-scene safety is the key milestone for fully autonomous driving, and reliable on-time prediction is essential to maintain safety in emergency scenarios. However, these emergency scenarios are long-tailed and hard to collect, which restricts the system from getting reliable predictions. In this paper, we build a new dataset, which aims at the long-term prediction with the inconspicuous state variation in history for the emergency event, named the Extro-Spective Prediction (ESP) problem. Based on the proposed dataset, a flexible feature encoder for ESP is introduced to various prediction methods as a seamless plug-in, and its consistent performance improvement underscores its efficacy. Furthermore, a new metric named clamped temporal error (CTE) is proposed to give a more comprehensive evaluation of prediction performance, especially in time-sensitive emergency events of subseconds. Interestingly, as our ESP features can be described in human-readable language naturally, the application of integrating into ChatGPT also shows huge potential. The ESP-dataset and all benchmarks are released at https://dingrui-wang.github.io/ESP-Dataset/. | [
"['Dingrui Wang' 'Zheyuan Lai' 'Yuda Li' 'Yi Wu' 'Yuexin Ma'\n 'Johannes Betz' 'Ruigang Yang' 'Wei Li']"
]
|
null | null | 2405.04101 | null | null | http://arxiv.org/pdf/2405.04101v1 | 2024-05-07T08:15:48Z | 2024-05-07T08:15:48Z | Continual Learning in the Presence of Repetition | Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design. | [
"['Hamed Hemati' 'Lorenzo Pellegrini' 'Xiaotian Duan' 'Zixuan Zhao'\n 'Fangfang Xia' 'Marc Masana' 'Benedikt Tscheschner' 'Eduardo Veas'\n 'Yuxiang Zheng' 'Shiji Zhao' 'Shao-Yuan Li' 'Sheng-Jun Huang'\n 'Vincenzo Lomonaco' 'Gido M. van de Ven']"
]
|
null | null | 2405.04111 | null | null | http://arxiv.org/pdf/2405.04111v1 | 2024-05-07T08:28:51Z | 2024-05-07T08:28:51Z | Adaptive Least Mean pth Power Graph Neural Networks | In the presence of impulsive noise, and missing observations, accurate online prediction of time-varying graph signals poses a crucial challenge in numerous application domains. We propose the Adaptive Least Mean $p^{th}$ Power Graph Neural Networks (LMP-GNN), a universal framework combining adaptive filter and graph neural network for online graph signal estimation. LMP-GNN retains the advantage of adaptive filtering in handling noise and missing observations as well as the online update capability. The incorporated graph neural network within the LMP-GNN can train and update filter parameters online instead of predefined filter parameters in previous methods, outputting more accurate prediction results. The adaptive update scheme of the LMP-GNN follows the solution of a $l_p$-norm optimization, rooting to the minimum dispersion criterion, and yields robust estimation results for time-varying graph signals under impulsive noise. A special case of LMP-GNN named the Sign-GNN is also provided and analyzed, Experiment results on two real-world datasets of temperature graph and traffic graph under four different noise distributions prove the effectiveness and robustness of our proposed LMP-GNN. | [
"['Changran Peng' 'Yi Yan' 'Ercan E. Kuruoglu']"
]
|
null | null | 2405.04114 | null | null | http://arxiv.org/pdf/2405.04114v1 | 2024-05-07T08:34:33Z | 2024-05-07T08:34:33Z | Acceleration Algorithms in GNNs: A Survey | Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based tasks. However, their inefficiency in training and inference presents challenges for scaling up to real-world and large-scale graph applications. To address the critical challenges, a range of algorithms have been proposed to accelerate training and inference of GNNs, attracting increasing attention from the research community. In this paper, we present a systematic review of acceleration algorithms in GNNs, which can be categorized into three main topics based on their purpose: training acceleration, inference acceleration, and execution acceleration. Specifically, we summarize and categorize the existing approaches for each main topic, and provide detailed characterizations of the approaches within each category. Additionally, we review several libraries related to acceleration algorithms in GNNs and discuss our Scalable Graph Learning (SGL) library. Finally, we propose promising directions for future research. A complete summary is presented in our GitHub repository: https://github.com/PKU-DAIR/SGL/blob/main/Awsome-GNN-Acceleration.md. | [
"['Lu Ma' 'Zeang Sheng' 'Xunkai Li' 'Xinyi Gao' 'Zhezheng Hao' 'Ling Yang'\n 'Wentao Zhang' 'Bin Cui']"
]
|
null | null | 2405.04118 | null | null | http://arxiv.org/pdf/2405.04118v1 | 2024-05-07T08:40:21Z | 2024-05-07T08:40:21Z | Policy Learning with a Language Bottleneck | Modern AI systems such as self-driving cars and game-playing agents achieve superhuman performance, but often lack human-like features such as generalization, interpretability and human inter-operability. Inspired by the rich interactions between language and decision-making in humans, we introduce Policy Learning with a Language Bottleneck (PLLB), a framework enabling AI agents to generate linguistic rules that capture the strategies underlying their most rewarding behaviors. PLLB alternates between a rule generation step guided by language models, and an update step where agents learn new policies guided by rules. In a two-player communication game, a maze solving task, and two image reconstruction tasks, we show that PLLB agents are not only able to learn more interpretable and generalizable behaviors, but can also share the learned rules with human users, enabling more effective human-AI coordination. | [
"['Megha Srivastava' 'Cedric Colas' 'Dorsa Sadigh' 'Jacob Andreas']"
]
|
null | null | 2405.04122 | null | null | http://arxiv.org/pdf/2405.04122v1 | 2024-05-07T08:44:29Z | 2024-05-07T08:44:29Z | Ranking-based Client Selection with Imitation Learning for Efficient
Federated Learning | Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round critically affects both the model performance and training efficiency, especially given the vast heterogeneity in training capabilities and data distribution across devices. To address these challenges, we introduce a novel device selection solution called FedRank, which is an end-to-end, ranking-based approach that is pre-trained by imitation learning against state-of-the-art analytical approaches. It not only considers data and system heterogeneity at runtime but also adaptively and efficiently chooses the most suitable clients for model training. Specifically, FedRank views client selection in FL as a ranking problem and employs a pairwise training strategy for the smart selection process. Additionally, an imitation learning-based approach is designed to counteract the cold-start issues often seen in state-of-the-art learning-based approaches. Experimental results reveal that model~ boosts model accuracy by 5.2% to 56.9%, accelerates the training convergence up to $2.01 times$ and saves the energy consumption up to $40.1%$. | [
"['Chunlin Tian' 'Zhan Shi' 'Xinpeng Qin' 'Li Li' 'Chengzhong Xu']"
]
|
null | null | 2405.04126 | null | null | http://arxiv.org/pdf/2405.04126v1 | 2024-05-07T08:50:25Z | 2024-05-07T08:50:25Z | Refining Joint Text and Source Code Embeddings for Retrieval Task with
Parameter-Efficient Fine-Tuning | The latest developments in Natural Language Processing (NLP) have demonstrated remarkable progress in a code-text retrieval problem. As the Transformer-based models used in this task continue to increase in size, the computational costs and time required for end-to-end fine-tuning become substantial. This poses a significant challenge for adapting and utilizing these models when computational resources are limited. Motivated by these concerns, we propose a fine-tuning framework that leverages Parameter-Efficient Fine-Tuning (PEFT) techniques. Moreover, we adopt contrastive learning objectives to improve the quality of bimodal representations learned by transformer models. Additionally, for PEFT methods we provide extensive benchmarking, the lack of which has been highlighted as a crucial problem in the literature. Based on the thorough experimentation with the CodeT5+ model conducted on two datasets, we demonstrate that the proposed fine-tuning framework has the potential to improve code-text retrieval performance by tuning only 0.4% parameters at most. | [
"['Karim Galliamov' 'Leila Khaertdinova' 'Karina Denisova']"
]
|
null | null | 2405.04134 | null | null | http://arxiv.org/pdf/2405.04134v1 | 2024-05-07T09:01:02Z | 2024-05-07T09:01:02Z | Geometry and Dynamics of LayerNorm | A technical note aiming to offer deeper intuition for the LayerNorm function common in deep neural networks. LayerNorm is defined relative to a distinguished 'neural' basis, but it does more than just normalize the corresponding vector elements. Rather, it implements a composition -- of linear projection, nonlinear scaling, and then affine transformation -- on input activation vectors. We develop both a new mathematical expression and geometric intuition, to make the net effect more transparent. We emphasize that, when LayerNorm acts on an N-dimensional vector space, all outcomes of LayerNorm lie within the intersection of an (N-1)-dimensional hyperplane and the interior of an N-dimensional hyperellipsoid. This intersection is the interior of an (N-1)-dimensional hyperellipsoid, and typical inputs are mapped near its surface. We find the direction and length of the principal axes of this (N-1)-dimensional hyperellipsoid via the eigen-decomposition of a simply constructed matrix. | [
"['Paul M. Riechers']"
]
|
null | null | 2405.04147 | null | null | http://arxiv.org/pdf/2405.04147v1 | 2024-05-07T09:26:20Z | 2024-05-07T09:26:20Z | Multiparameter regularization and aggregation in the context of
polynomial functional regression | Most of the recent results in polynomial functional regression have been focused on an in-depth exploration of single-parameter regularization schemes. In contrast, in this study we go beyond that framework by introducing an algorithm for multiple parameter regularization and presenting a theoretically grounded method for dealing with the associated parameters. This method facilitates the aggregation of models with varying regularization parameters. The efficacy of the proposed approach is assessed through evaluations on both synthetic and some real-world medical data, revealing promising results. | [
"['Elke R. Gizewski' 'Markus Holzleitner' 'Lukas Mayer-Suess'\n 'Sergiy Pereverzyev Jr.' 'Sergei V. Pereverzyev']"
]
|
null | null | 2405.04151 | null | null | http://arxiv.org/pdf/2405.04151v1 | 2024-05-07T09:41:39Z | 2024-05-07T09:41:39Z | Gas Source Localization Using physics Guided Neural Networks | This work discusses a novel method for estimating the location of a gas source based on spatially distributed concentration measurements taken, e.g., by a mobile robot or flying platform that follows a predefined trajectory to collect samples. The proposed approach uses a Physics-Guided Neural Network to approximate the gas dispersion with the source location as an additional network input. After an initial offline training phase, the neural network can be used to efficiently solve the inverse problem of localizing the gas source based on measurements. The proposed approach allows avoiding rather costly numerical simulations of gas physics needed for solving inverse problems. Our experiments show that the method localizes the source well, even when dealing with measurements affected by noise. | [
"['Victor Scott Prieto Ruiz' 'Patrick Hinsen' 'Thomas Wiedemann'\n 'Constantin Christof' 'Dmitriy Shutin']"
]
|
null | null | 2405.04156 | null | null | http://arxiv.org/pdf/2405.04156v1 | 2024-05-07T09:50:57Z | 2024-05-07T09:50:57Z | How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit
via Mechanistic Interpretability | Transformer-based language models are treated as black-boxes because of their large number of parameters and complex internal interactions, which is a serious safety concern. Mechanistic Interpretability (MI) intends to reverse-engineer neural network behaviors in terms of human-understandable components. In this work, we focus on understanding how GPT-2 Small performs the task of predicting three-letter acronyms. Previous works in the MI field have focused so far on tasks that predict a single token. To the best of our knowledge, this is the first work that tries to mechanistically understand a behavior involving the prediction of multiple consecutive tokens. We discover that the prediction is performed by a circuit composed of 8 attention heads (~5% of the total heads) which we classified in three groups according to their role. We also demonstrate that these heads concentrate the acronym prediction functionality. In addition, we mechanistically interpret the most relevant heads of the circuit and find out that they use positional information which is propagated via the causal mask mechanism. We expect this work to lay the foundation for understanding more complex behaviors involving multiple-token predictions. | [
"['Jorge García-Carrasco' 'Alejandro Maté' 'Juan Trujillo']"
]
|
null | null | 2405.04161 | null | null | http://arxiv.org/pdf/2405.04161v1 | 2024-05-07T09:58:02Z | 2024-05-07T09:58:02Z | Opportunities for machine learning in scientific discovery | Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques across various fields. However, our ability to leverage ML methods for scientific discovery, {it i.e.} to obtain fundamental and formalized knowledge about natural processes, is still in its infancy. In this review, we explore how the scientific community can increasingly leverage ML techniques to achieve scientific discoveries. We observe that the applicability and opportunity of ML depends strongly on the nature of the problem domain, and whether we have full ({it e.g.}, turbulence), partial ({it e.g.}, computational biochemistry), or no ({it e.g.}, neuroscience) {it a-priori} knowledge about the governing equations and physical properties of the system. Although challenges remain, principled use of ML is opening up new avenues for fundamental scientific discoveries. Throughout these diverse fields, there is a theme that ML is enabling researchers to embrace complexity in observational data that was previously intractable to classic analysis and numerical investigations. | [
"['Ricardo Vinuesa' 'Jean Rabault' 'Hossein Azizpour' 'Stefan Bauer'\n 'Bingni W. Brunton' 'Arne Elofsson' 'Elias Jarlebring'\n 'Hedvig Kjellstrom' 'Stefano Markidis' 'David Marlevi' 'Paola Cinnella'\n 'Steven L. Brunton']"
]
|
null | null | 2405.04171 | null | null | http://arxiv.org/pdf/2405.04171v1 | 2024-05-07T10:11:42Z | 2024-05-07T10:11:42Z | FedStale: leveraging stale client updates in federated learning | Federated learning algorithms, such as FedAvg, are negatively affected by data heterogeneity and partial client participation. To mitigate the latter problem, global variance reduction methods, like FedVARP, leverage stale model updates for non-participating clients. These methods are effective under homogeneous client participation. Yet, this paper shows that, when some clients participate much less than others, aggregating updates with different levels of staleness can detrimentally affect the training process. Motivated by this observation, we introduce FedStale, a novel algorithm that updates the global model in each round through a convex combination of "fresh" updates from participating clients and "stale" updates from non-participating ones. By adjusting the weight in the convex combination, FedStale interpolates between FedAvg, which only uses fresh updates, and FedVARP, which treats fresh and stale updates equally. Our analysis of FedStale convergence yields the following novel findings: i) it integrates and extends previous FedAvg and FedVARP analyses to heterogeneous client participation; ii) it underscores how the least participating client influences convergence error; iii) it provides practical guidelines to best exploit stale updates, showing that their usefulness diminishes as data heterogeneity decreases and participation heterogeneity increases. Extensive experiments featuring diverse levels of client data and participation heterogeneity not only confirm these findings but also show that FedStale outperforms both FedAvg and FedVARP in many settings. | [
"['Angelo Rodio' 'Giovanni Neglia']"
]
|
null | null | 2405.04180 | null | null | http://arxiv.org/pdf/2405.04180v1 | 2024-05-07T10:39:14Z | 2024-05-07T10:39:14Z | Sora Detector: A Unified Hallucination Detection for Large Text-to-Video
Models | The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis of high-fidelity video content guided by textual descriptions. Despite this significant progress, these models are often susceptible to hallucination, generating contents that contradict the input text, which poses a challenge to their reliability and practical deployment. To address this critical issue, we introduce the SoraDetector, a novel unified framework designed to detect hallucinations across diverse large T2V models, including the cutting-edge Sora model. Our framework is built upon a comprehensive analysis of hallucination phenomena, categorizing them based on their manifestation in the video content. Leveraging the state-of-the-art keyframe extraction techniques and multimodal large language models, SoraDetector first evaluates the consistency between extracted video content summary and textual prompts, then constructs static and dynamic knowledge graphs (KGs) from frames to detect hallucination both in single frames and across frames. Sora Detector provides a robust and quantifiable measure of consistency, static and dynamic hallucination. In addition, we have developed the Sora Detector Agent to automate the hallucination detection process and generate a complete video quality report for each input video. Lastly, we present a novel meta-evaluation benchmark, T2VHaluBench, meticulously crafted to facilitate the evaluation of advancements in T2V hallucination detection. Through extensive experiments on videos generated by Sora and other large T2V models, we demonstrate the efficacy of our approach in accurately detecting hallucinations. The code and dataset can be accessed via GitHub. | [
"['Zhixuan Chu' 'Lei Zhang' 'Yichen Sun' 'Siqiao Xue' 'Zhibo Wang'\n 'Zhan Qin' 'Kui Ren']"
]
|
null | null | 2405.04181 | null | null | http://arxiv.org/pdf/2405.04181v2 | 2024-05-22T09:31:21Z | 2024-05-07T10:39:19Z | Detecting music deepfakes is easy but actually hard | In the face of a new era of generative models, the detection of artificially generated content has become a matter of utmost importance. The ability to create credible minute-long music deepfakes in a few seconds on user-friendly platforms poses a real threat of fraud on streaming services and unfair competition to human artists. This paper demonstrates the possibility (and surprising ease) of training classifiers on datasets comprising real audio and fake reconstructions, achieving a convincing accuracy of 99.8%. To our knowledge, this marks the first publication of a music deepfake detector, a tool that will help in the regulation of music forgery. Nevertheless, informed by decades of literature on forgery detection in other fields, we stress that a good test score is not the end of the story. We step back from the straightforward ML framework and expose many facets that could be problematic with such a deployed detector: calibration, robustness to audio manipulation, generalisation to unseen models, interpretability and possibility for recourse. This second part acts as a position for future research steps in the field and a caveat to a flourishing market of fake content checkers. | [
"['Darius Afchar' 'Gabriel Meseguer-Brocal' 'Romain Hennequin']"
]
|
null | null | 2405.04191 | null | null | http://arxiv.org/pdf/2405.04191v1 | 2024-05-07T10:53:20Z | 2024-05-07T10:53:20Z | Effective and Robust Adversarial Training against Data and Label
Corruptions | Corruptions due to data perturbations and label noise are prevalent in the datasets from unreliable sources, which poses significant threats to model training. Despite existing efforts in developing robust models, current learning methods commonly overlook the possible co-existence of both corruptions, limiting the effectiveness and practicability of the model. In this paper, we develop an Effective and Robust Adversarial Training (ERAT) framework to simultaneously handle two types of corruption (i.e., data and label) without prior knowledge of their specifics. We propose a hybrid adversarial training surrounding multiple potential adversarial perturbations, alongside a semi-supervised learning based on class-rebalancing sample selection to enhance the resilience of the model for dual corruption. On the one hand, in the proposed adversarial training, the perturbation generation module learns multiple surrogate malicious data perturbations by taking a DNN model as the victim, while the model is trained to maintain semantic consistency between the original data and the hybrid perturbed data. It is expected to enable the model to cope with unpredictable perturbations in real-world data corruption. On the other hand, a class-rebalancing data selection strategy is designed to fairly differentiate clean labels from noisy labels. Semi-supervised learning is performed accordingly by discarding noisy labels. Extensive experiments demonstrate the superiority of the proposed ERAT framework. | [
"['Peng-Fei Zhang' 'Zi Huang' 'Xin-Shun Xu' 'Guangdong Bai']"
]
|
null | null | 2405.04206 | null | null | http://arxiv.org/pdf/2405.04206v1 | 2024-05-07T11:20:10Z | 2024-05-07T11:20:10Z | NOVA: NoC-based Vector Unit for Mapping Attention Layers on a CNN
Accelerator | Attention mechanisms are becoming increasingly popular, being used in neural network models in multiple domains such as natural language processing (NLP) and vision applications, especially at the edge. However, attention layers are difficult to map onto existing neuro accelerators since they have a much higher density of non-linear operations, which lead to inefficient utilization of today's vector units. This work introduces NOVA, a NoC-based Vector Unit that can perform non-linear operations within the NoC of the accelerators, and can be overlaid onto existing neuro accelerators to map attention layers at the edge. Our results show that the NOVA architecture is up to 37.8x more power-efficient than state-of-the-art hardware approximators when running existing attention-based neural networks. | [
"['Mohit Upadhyay' 'Rohan Juneja' 'Weng-Fai Wong' 'Li-Shiuan Peh']"
]
|
null | null | 2405.04212 | null | null | http://arxiv.org/pdf/2405.04212v1 | 2024-05-07T11:24:56Z | 2024-05-07T11:24:56Z | Green Tsetlin Redefining Efficiency in Tsetlin Machine Frameworks | Green Tsetlin (GT) is a Tsetlin Machine (TM) framework developed to solve real-world problems using TMs. Several frameworks already exist that provide access to TM implementations. However, these either lack features or have a research-first focus. GT is an easy-to-use framework that aims to lower the complexity and provide a production-ready TM implementation that is great for experienced practitioners and beginners. To this end, GT establishes a clear separation between training and inference. A C++ backend with a Python interface provides competitive training and inference performance, with the option of running in pure Python. It also integrates support for critical components such as exporting trained models, hyper-parameter search, and cross-validation out-of-the-box. | [
"['Sondre Glimsdal' 'Sebastian Østby' 'Tobias M. Brambo' 'Eirik M. Vinje']"
]
|
null | null | 2405.04230 | null | null | http://arxiv.org/pdf/2405.04230v1 | 2024-05-07T11:50:25Z | 2024-05-07T11:50:25Z | Unveiling the optimization process of Physics Informed Neural Networks:
How accurate and competitive can PINNs be? | This study investigates the potential accuracy boundaries of physics-informed neural networks, contrasting their approach with previous similar works and traditional numerical methods. We find that selecting improved optimization algorithms significantly enhances the accuracy of the results. Simple modifications to the loss function may also improve precision, offering an additional avenue for enhancement. Despite optimization algorithms having a greater impact on convergence than adjustments to the loss function, practical considerations often favor tweaking the latter due to ease of implementation. On a global scale, the integration of an enhanced optimizer and a marginally adjusted loss function enables a reduction in the loss function by several orders of magnitude across diverse physical problems. Consequently, our results obtained using compact networks (typically comprising 2 or 3 layers of 20-30 neurons) achieve accuracies comparable to finite difference schemes employing thousands of grid points. This study encourages the continued advancement of PINNs and associated optimization techniques for broader applications across various fields. | [
"['Jorge F. Urbán' 'Petros Stefanou' 'José A. Pons']"
]
|
null | null | 2405.04233 | null | null | http://arxiv.org/pdf/2405.04233v1 | 2024-05-07T11:52:49Z | 2024-05-07T11:52:49Z | Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator
with Diffusion Models | We introduce Vidu, a high-performance text-to-video generator that is capable of producing 1080p videos up to 16 seconds in a single generation. Vidu is a diffusion model with U-ViT as its backbone, which unlocks the scalability and the capability for handling long videos. Vidu exhibits strong coherence and dynamism, and is capable of generating both realistic and imaginative videos, as well as understanding some professional photography techniques, on par with Sora -- the most powerful reported text-to-video generator. Finally, we perform initial experiments on other controllable video generation, including canny-to-video generation, video prediction and subject-driven generation, which demonstrate promising results. | [
"['Fan Bao' 'Chendong Xiang' 'Gang Yue' 'Guande He' 'Hongzhou Zhu'\n 'Kaiwen Zheng' 'Min Zhao' 'Shilong Liu' 'Yaole Wang' 'Jun Zhu']"
]
|
null | null | 2405.04235 | null | null | http://arxiv.org/pdf/2405.04235v1 | 2024-05-07T11:54:22Z | 2024-05-07T11:54:22Z | LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe
Diffusion-based Planning | Operating effectively in complex environments while complying with specified constraints is crucial for the safe and successful deployment of robots that interact with and operate around people. In this work, we focus on generating long-horizon trajectories that adhere to novel static and temporally-extended constraints/instructions at test time. We propose a data-driven diffusion-based framework, LTLDoG, that modifies the inference steps of the reverse process given an instruction specified using finite linear temporal logic ($text{LTL}_f$). LTLDoG leverages a satisfaction value function on $text{LTL}_f$ and guides the sampling steps using its gradient field. This value function can also be trained to generalize to new instructions not observed during training, enabling flexible test-time adaptability. Experiments in robot navigation and manipulation illustrate that the method is able to generate trajectories that satisfy formulae that specify obstacle avoidance and visitation sequences. | [
"['Zeyu Feng' 'Hao Luan' 'Pranav Goyal' 'Harold Soh']"
]
|
null | null | 2405.04245 | null | null | http://arxiv.org/pdf/2405.04245v2 | 2024-05-16T06:51:23Z | 2024-05-07T12:02:23Z | Exploring Correlations of Self-Supervised Tasks for Graphs | Graph self-supervised learning has sparked a research surge in training informative representations without accessing any labeled data. However, our understanding of graph self-supervised learning remains limited, and the inherent relationships between various self-supervised tasks are still unexplored. Our paper aims to provide a fresh understanding of graph self-supervised learning based on task correlations. Specifically, we evaluate the performance of the representations trained by one specific task on other tasks and define correlation values to quantify task correlations. Through this process, we unveil the task correlations between various self-supervised tasks and can measure their expressive capabilities, which are closely related to downstream performance. By analyzing the correlation values between tasks across various datasets, we reveal the complexity of task correlations and the limitations of existing multi-task learning methods. To obtain more capable representations, we propose Graph Task Correlation Modeling (GraphTCM) to illustrate the task correlations and utilize it to enhance graph self-supervised training. The experimental results indicate that our method significantly outperforms existing methods across various downstream tasks. | [
"['Taoran Fang' 'Wei Zhou' 'Yifei Sun' 'Kaiqiao Han' 'Lvbin Ma' 'Yang Yang']"
]
|
null | null | 2405.04249 | null | null | http://arxiv.org/pdf/2405.04249v1 | 2024-05-07T12:07:06Z | 2024-05-07T12:07:06Z | Federated Learning for Cooperative Inference Systems: The Case of Early
Exit Networks | As Internet of Things (IoT) technology advances, end devices like sensors and smartphones are progressively equipped with AI models tailored to their local memory and computational constraints. Local inference reduces communication costs and latency; however, these smaller models typically underperform compared to more sophisticated models deployed on edge servers or in the cloud. Cooperative Inference Systems (CISs) address this performance trade-off by enabling smaller devices to offload part of their inference tasks to more capable devices. These systems often deploy hierarchical models that share numerous parameters, exemplified by Deep Neural Networks (DNNs) that utilize strategies like early exits or ordered dropout. In such instances, Federated Learning (FL) may be employed to jointly train the models within a CIS. Yet, traditional training methods have overlooked the operational dynamics of CISs during inference, particularly the potential high heterogeneity in serving rates across clients. To address this gap, we propose a novel FL approach designed explicitly for use in CISs that accounts for these variations in serving rates. Our framework not only offers rigorous theoretical guarantees, but also surpasses state-of-the-art (SOTA) training algorithms for CISs, especially in scenarios where inference request rates or data availability are uneven among clients. | [
"['Caelin Kaplan' 'Tareq Si Salem' 'Angelo Rodio' 'Chuan Xu'\n 'Giovanni Neglia']"
]
|
null | null | 2405.04251 | null | null | http://arxiv.org/pdf/2405.04251v1 | 2024-05-07T12:11:15Z | 2024-05-07T12:11:15Z | A General Model for Detecting Learner Engagement: Implementation and
Evaluation | Considering learner engagement has a mutual benefit for both learners and instructors. Instructors can help learners increase their attention, involvement, motivation, and interest. On the other hand, instructors can improve their instructional performance by evaluating the cumulative results of all learners and upgrading their training programs. This paper proposes a general, lightweight model for selecting and processing features to detect learners' engagement levels while preserving the sequential temporal relationship over time. During training and testing, we analyzed the videos from the publicly available DAiSEE dataset to capture the dynamic essence of learner engagement. We have also proposed an adaptation policy to find new labels that utilize the affective states of this dataset related to education, thereby improving the models' judgment. The suggested model achieves an accuracy of 68.57% in a specific implementation and outperforms the studied state-of-the-art models detecting learners' engagement levels. | [
"['Somayeh Malekshahi' 'Javad M. Kheyridoost' 'Omid Fatemi']"
]
|
null | null | 2405.04252 | null | null | http://arxiv.org/pdf/2405.04252v1 | 2024-05-07T12:13:11Z | 2024-05-07T12:13:11Z | VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting | This paper presents VAEneu, an innovative autoregressive method for multistep ahead univariate probabilistic time series forecasting. We employ the conditional VAE framework and optimize the lower bound of the predictive distribution likelihood function by adopting the Continuous Ranked Probability Score (CRPS), a strictly proper scoring rule, as the loss function. This novel pipeline results in forecasting sharp and well-calibrated predictive distribution. Through a comprehensive empirical study, VAEneu is rigorously benchmarked against 12 baseline models across 12 datasets. The results unequivocally demonstrate VAEneu's remarkable forecasting performance. VAEneu provides a valuable tool for quantifying future uncertainties, and our extensive empirical study lays the foundation for future comparative studies for univariate multistep ahead probabilistic forecasting. | [
"['Alireza Koochali' 'Ensiye Tahaei' 'Andreas Dengel' 'Sheraz Ahmed']"
]
|
null | null | 2405.04260 | null | null | http://arxiv.org/pdf/2405.04260v2 | 2024-05-08T09:38:15Z | 2024-05-07T12:20:12Z | Verified Neural Compressed Sensing | We develop the first (to the best of our knowledge) provably correct neural networks for a precise computational task, with the proof of correctness generated by an automated verification algorithm without any human input. Prior work on neural network verification has focused on partial specifications that, even when satisfied, are not sufficient to ensure that a neural network never makes errors. We focus on applying neural network verification to computational tasks with a precise notion of correctness, where a verifiably correct neural network provably solves the task at hand with no caveats. In particular, we develop an approach to train and verify the first provably correct neural networks for compressed sensing, i.e., recovering sparse vectors from a number of measurements smaller than the dimension of the vector. We show that for modest problem dimensions (up to 50), we can train neural networks that provably recover a sparse vector from linear and binarized linear measurements. Furthermore, we show that the complexity of the network (number of neurons/layers) can be adapted to the problem difficulty and solve problems where traditional compressed sensing methods are not known to provably work. | [
"['Rudy Bunel' 'Krishnamurthy Dvijotham' 'M. Pawan Kumar'\n 'Alessandro De Palma' 'Robert Stanforth']"
]
|
null | null | 2405.04272 | null | null | http://arxiv.org/pdf/2405.04272v1 | 2024-05-07T12:41:31Z | 2024-05-07T12:41:31Z | BUDDy: Single-Channel Blind Unsupervised Dereverberation with Diffusion
Models | In this paper, we present an unsupervised single-channel method for joint blind dereverberation and room impulse response estimation, based on posterior sampling with diffusion models. We parameterize the reverberation operator using a filter with exponential decay for each frequency subband, and iteratively estimate the corresponding parameters as the speech utterance gets refined along the reverse diffusion trajectory. A measurement consistency criterion enforces the fidelity of the generated speech with the reverberant measurement, while an unconditional diffusion model implements a strong prior for clean speech generation. Without any knowledge of the room impulse response nor any coupled reverberant-anechoic data, we can successfully perform dereverberation in various acoustic scenarios. Our method significantly outperforms previous blind unsupervised baselines, and we demonstrate its increased robustness to unseen acoustic conditions in comparison to blind supervised methods. Audio samples and code are available online. | [
"['Eloi Moliner' 'Jean-Marie Lemercier' 'Simon Welker' 'Timo Gerkmann'\n 'Vesa Välimäki']"
]
|
null | null | 2405.04278 | null | null | http://arxiv.org/pdf/2405.04278v3 | 2024-05-22T08:46:26Z | 2024-05-07T12:46:45Z | Uncertainty Quantification Metrics for Deep Regression | When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predictive uncertainty. A reliable uncertainty allows downstream modules to reason about the safety of its actions. In this work, we address metrics for evaluating such an uncertainty. Specifically, we focus on regression tasks, and investigate Area Under Sparsification Error (AUSE), Calibration Error, Spearman's Rank Correlation, and Negative Log-Likelihood (NLL). Using synthetic regression datasets, we look into how those metrics behave under four typical types of uncertainty, their stability regarding the size of the test set, and reveal their strengths and weaknesses. Our results indicate that Calibration Error is the most stable and interpretable metric, but AUSE and NLL also have their respective use cases. We discourage the usage of Spearman's Rank Correlation for evaluating uncertainties and recommend replacing it with AUSE. | [
"['Simon Kristoffersson Lind' 'Ziliang Xiong' 'Per-Erik Forssén'\n 'Volker Krüger']"
]
|
null | null | 2405.04288 | null | null | http://arxiv.org/pdf/2405.04288v1 | 2024-05-05T21:08:49Z | 2024-05-05T21:08:49Z | BetterNet: An Efficient CNN Architecture with Residual Learning and
Attention for Precision Polyp Segmentation | Colorectal cancer contributes significantly to cancer-related mortality. Timely identification and elimination of polyps through colonoscopy screening is crucial in order to decrease mortality rates. Accurately detecting polyps in colonoscopy images is difficult because of the differences in characteristics such as size, shape, texture, and similarity to surrounding tissues. Current deep-learning methods often face difficulties in capturing long-range connections necessary for segmentation. This research presents BetterNet, a convolutional neural network (CNN) architecture that combines residual learning and attention methods to enhance the accuracy of polyp segmentation. The primary characteristics encompass (1) a residual decoder architecture that facilitates efficient gradient propagation and integration of multiscale features. (2) channel and spatial attention blocks within the decoder block to concentrate the learning process on the relevant areas of polyp regions. (3) Achieving state-of-the-art performance on polyp segmentation benchmarks while still ensuring computational efficiency. (4) Thorough ablation tests have been conducted to confirm the influence of architectural components. (5) The model code has been made available as open-source for further contribution. Extensive evaluations conducted on datasets such as Kvasir-SEG, CVC ClinicDB, Endoscene, EndoTect, and Kvasir-Sessile demonstrate that BetterNets outperforms current SOTA models in terms of segmentation accuracy by significant margins. The lightweight design enables real-time inference for various applications. BetterNet shows promise in integrating computer-assisted diagnosis techniques to enhance the detection of polyps and the early recognition of cancer. Link to the code: https://github.com/itsOwen/BetterNet | [
"['Owen Singh' 'Sandeep Singh Sengar']"
]
|
null | null | 2405.04296 | null | null | http://arxiv.org/pdf/2405.04296v1 | 2024-05-07T13:11:37Z | 2024-05-07T13:11:37Z | Open Implementation and Study of BEST-RQ for Speech Processing | Self-Supervised Learning (SSL) has proven to be useful in various speech tasks. However, these methods are generally very demanding in terms of data, memory, and computational resources. BERT-based Speech pre-Training with Random-projection Quantizer (BEST-RQ), is an SSL method that has shown great performance on Automatic Speech Recognition (ASR) while being simpler than other SSL methods, such as wav2vec 2.0. Despite BEST-RQ's great performance, details are lacking in the original paper, such as the amount of GPU/TPU hours used in pre-training, and there is no official easy-to-use open-source implementation. Furthermore, BEST-RQ has not been evaluated on other downstream tasks aside from ASR and speech translation. In this work, we describe a re-implementation of a Random-projection quantizer and perform a preliminary study with a comparison to wav2vec 2.0 on four downstream tasks. We discuss the details and differences of our implementation. We show that a random projection quantizer can achieve similar downstream performance as wav2vec 2.0 while decreasing training time by over a factor of two. | [
"['Ryan Whetten' 'Titouan Parcollet' 'Marco Dinarelli' 'Yannick Estève']"
]
|
null | null | 2405.04307 | null | null | http://arxiv.org/pdf/2405.04307v1 | 2024-05-07T13:29:41Z | 2024-05-07T13:29:41Z | Improving Offline Reinforcement Learning with Inaccurate Simulators | Offline reinforcement learning (RL) provides a promising approach to avoid costly online interaction with the real environment. However, the performance of offline RL highly depends on the quality of the datasets, which may cause extrapolation error in the learning process. In many robotic applications, an inaccurate simulator is often available. However, the data directly collected from the inaccurate simulator cannot be directly used in offline RL due to the well-known exploration-exploitation dilemma and the dynamic gap between inaccurate simulation and the real environment. To address these issues, we propose a novel approach to combine the offline dataset and the inaccurate simulation data in a better manner. Specifically, we pre-train a generative adversarial network (GAN) model to fit the state distribution of the offline dataset. Given this, we collect data from the inaccurate simulator starting from the distribution provided by the generator and reweight the simulated data using the discriminator. Our experimental results in the D4RL benchmark and a real-world manipulation task confirm that our method can benefit more from both inaccurate simulator and limited offline datasets to achieve better performance than the state-of-the-art methods. | [
"['Yiwen Hou' 'Haoyuan Sun' 'Jinming Ma' 'Feng Wu']"
]
|
null | null | 2405.04321 | null | null | http://arxiv.org/pdf/2405.04321v1 | 2024-05-07T13:47:35Z | 2024-05-07T13:47:35Z | Molecular Identification via Molecular Fingerprint extraction from
Atomic Force Microscopy images | Non--Contact Atomic Force Microscopy with CO--functionalized metal tips (referred to as HR-AFM) provides access to the internal structure of individual molecules adsorbed on a surface with totally unprecedented resolution. Previous works have shown that deep learning (DL) models can retrieve the chemical and structural information encoded in a 3D stack of constant-height HR--AFM images, leading to molecular identification. In this work, we overcome their limitations by using a well-established description of the molecular structure in terms of topological fingerprints, the 1024--bit Extended Connectivity Chemical Fingerprints of radius 2 (ECFP4), that were developed for substructure and similarity searching. ECFPs provide local structural information of the molecule, each bit correlating with a particular substructure within the molecule. Our DL model is able to extract this optimized structural descriptor from the 3D HR--AFM stacks and use it, through virtual screening, to identify molecules from their predicted ECFP4 with a retrieval accuracy on theoretical images of 95.4%. Furthermore, this approach, unlike previous DL models, assigns a confidence score, the Tanimoto similarity, to each of the candidate molecules, thus providing information on the reliability of the identification. By construction, the number of times a certain substructure is present in the molecule is lost during the hashing process, necessary to make them useful for machine learning applications. We show that it is possible to complement the fingerprint-based virtual screening with global information provided by another DL model that predicts from the same HR--AFM stacks the chemical formula, boosting the identification accuracy up to a 97.6%. Finally, we perform a limited test with experimental images, obtaining promising results towards the application of this pipeline under real conditions | [
"['Manuel González Lastre' 'Pablo Pou' 'Miguel Wiche' 'Daniel Ebeling'\n 'Andre Schirmeisen' 'Rubén Pérez']"
]
|
null | null | 2405.04342 | null | null | http://arxiv.org/pdf/2405.04342v1 | 2024-05-07T14:14:50Z | 2024-05-07T14:14:50Z | The Curse of Diversity in Ensemble-Based Exploration | We uncover a surprising phenomenon in deep reinforcement learning: training a diverse ensemble of data-sharing agents -- a well-established exploration strategy -- can significantly impair the performance of the individual ensemble members when compared to standard single-agent training. Through careful analysis, we attribute the degradation in performance to the low proportion of self-generated data in the shared training data for each ensemble member, as well as the inefficiency of the individual ensemble members to learn from such highly off-policy data. We thus name this phenomenon the curse of diversity. We find that several intuitive solutions -- such as a larger replay buffer or a smaller ensemble size -- either fail to consistently mitigate the performance loss or undermine the advantages of ensembling. Finally, we demonstrate the potential of representation learning to counteract the curse of diversity with a novel method named Cross-Ensemble Representation Learning (CERL) in both discrete and continuous control domains. Our work offers valuable insights into an unexpected pitfall in ensemble-based exploration and raises important caveats for future applications of similar approaches. | [
"['Zhixuan Lin' \"Pierluca D'Oro\" 'Evgenii Nikishin' 'Aaron Courville']"
]
|
null | null | 2405.04346 | null | null | http://arxiv.org/pdf/2405.04346v1 | 2024-05-07T14:23:22Z | 2024-05-07T14:23:22Z | Revisiting character-level adversarial attacks | Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adversarial examples. While character-level attacks easily maintain semantics, they have received less attention as they cannot easily adopt popular gradient-based methods, and are thought to be easy to defend. Challenging these beliefs, we introduce Charmer, an efficient query-based adversarial attack capable of achieving high attack success rate (ASR) while generating highly similar adversarial examples. Our method successfully targets both small (BERT) and large (Llama 2) models. Specifically, on BERT with SST-2, Charmer improves the ASR in 4.84% points and the USE similarity in 8% points with respect to the previous art. Our implementation is available in https://github.com/LIONS-EPFL/Charmer. | [
"['Elias Abad Rocamora' 'Yongtao Wu' 'Fanghui Liu' 'Grigorios G. Chrysos'\n 'Volkan Cevher']"
]
|
null | null | 2405.04363 | null | null | http://arxiv.org/pdf/2405.04363v2 | 2024-05-14T08:02:36Z | 2024-05-07T14:44:41Z | Some Notes on the Sample Complexity of Approximate Channel Simulation | Channel simulation algorithms can efficiently encode random samples from a prescribed target distribution $Q$ and find applications in machine learning-based lossy data compression. However, algorithms that encode exact samples usually have random runtime, limiting their applicability when a consistent encoding time is desirable. Thus, this paper considers approximate schemes with a fixed runtime instead. First, we strengthen a result of Agustsson and Theis and show that there is a class of pairs of target distribution $Q$ and coding distribution $P$, for which the runtime of any approximate scheme scales at least super-polynomially in $D_infty[Q Vert P]$. We then show, by contrast, that if we have access to an unnormalised Radon-Nikodym derivative $r propto dQ/dP$ and knowledge of $D_{KL}[Q Vert P]$, we can exploit global-bound, depth-limited A* coding to ensure $mathrm{TV}[Q Vert P] leq epsilon$ and maintain optimal coding performance with a sample complexity of only $exp_2big((D_{KL}[Q Vert P] + o(1)) big/ epsilonbig)$. | [
"['Gergely Flamich' 'Lennie Wells']"
]
|
null | null | 2405.04372 | null | null | http://arxiv.org/pdf/2405.04372v2 | 2024-05-09T09:46:35Z | 2024-05-07T14:55:42Z | Explainable machine learning for predicting shellfish toxicity in the
Adriatic Sea using long-term monitoring data of HABs | In this study, explainable machine learning techniques are applied to predict the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by harmful algal blooms. By analysing a newly created 28-year dataset containing records of toxic phytoplankton in mussel farming areas and toxin concentrations in mussels (Mytilus galloprovincialis), we train and evaluate the performance of ML models to accurately predict diarrhetic shellfish poisoning (DSP) events. The random forest model provided the best prediction of positive toxicity results based on the F1 score. Explainability methods such as permutation importance and SHAP identified key species (Dinophysis fortii and D. caudata) and environmental factors (salinity, river discharge and precipitation) as the best predictors of DSP outbreaks. These findings are important for improving early warning systems and supporting sustainable aquaculture practices. | [
"['Martin Marzidovšek' 'Janja Francé' 'Vid Podpečan' 'Stanka Vadnjal'\n 'Jožica Dolenc' 'Patricija Mozetič']"
]
|
null | null | 2405.04376 | null | null | http://arxiv.org/pdf/2405.04376v3 | 2024-05-27T14:46:21Z | 2024-05-07T14:58:12Z | Towards Stability of Parameter-free Optimization | Hyperparameter tuning, particularly the selection of an appropriate learning rate in adaptive gradient training methods, remains a challenge. To tackle this challenge, in this paper, we propose a novel parameter-free optimizer, textsc{AdamG} (Adam with the golden step size), designed to automatically adapt to diverse optimization problems without manual tuning. The core technique underlying textsc{AdamG} is our golden step size derived for the AdaGrad-Norm algorithm, which is expected to help AdaGrad-Norm preserve the tuning-free convergence and approximate the optimal step size in expectation w.r.t. various optimization scenarios. To better evaluate tuning-free performance, we propose a novel evaluation criterion, textit{reliability}, to comprehensively assess the efficacy of parameter-free optimizers in addition to classical performance criteria. Empirical results demonstrate that compared with other parameter-free baselines, textsc{AdamG} achieves superior performance, which is consistently on par with Adam using a manually tuned learning rate across various optimization tasks. | [
"['Yijiang Pang' 'Shuyang Yu' 'Bao Hoang' 'Jiayu Zhou']"
]
|
null | null | 2405.04386 | null | null | http://arxiv.org/pdf/2405.04386v1 | 2024-05-07T15:11:42Z | 2024-05-07T15:11:42Z | Pragmatist Intelligence: Where the Principle of Usefulness Can Take ANNs | Artificial neural networks (ANNs) perform extraordinarily on numerous tasks including classification or prediction, e.g., speech processing and image classification. These new functions are based on a computational model that is enabled to select freely all necessary internal model parameters as long as it eventually delivers the functionality it is supposed to exhibit. Here, we review the connection between the model parameter selection in machine learning (ML) algorithms running on ANNs and the epistemological theory of neopragmatism focusing on the theory's utility and anti-representationalist aspects. To understand the consequences of the model parameter selection of an ANN, we suggest using neopragmatist theories whose implications are well studied. Incidentally, neopragmatism's notion of optimization is also based on utility considerations. This means that applying this approach elegantly reveals the inherent connections between optimization in ML, using a numerical method during the learning phase, and optimization in the ethical theory of consequentialism, where it occurs as a maxim of action. We suggest that these connections originate from the way relevance is calculated in ML systems. This could ultimately reveal a tendency for specific actions in ML systems. | [
"['Antonio Bikić' 'Sayan Mukherjee']"
]
|
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