categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2402.17317
| null | null |
http://arxiv.org/pdf/2402.17317v1
|
2024-02-27T08:49:30Z
|
2024-02-27T08:49:30Z
|
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced
Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
|
Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for brain tumour segmentation, the first task of the BraTS2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other's knowledge gaps. Using the new metric, our best solution achieves the dice results 0.9005, 0.8673, 0.8509 and HD95 14.940, 14.467, 17.699 (whole tumour, tumour core and enhancing tumour) in the validation set.
|
[
"['André Ferreira' 'Naida Solak' 'Jianning Li' 'Philipp Dammann'\n 'Jens Kleesiek' 'Victor Alves' 'Jan Egger']"
] |
null | null |
2402.17318
| null | null |
http://arxiv.org/pdf/2402.17318v1
|
2024-02-27T08:50:45Z
|
2024-02-27T08:50:45Z
|
Scaling Supervised Local Learning with Augmented Auxiliary Networks
|
Deep neural networks are typically trained using global error signals that backpropagate (BP) end-to-end, which is not only biologically implausible but also suffers from the update locking problem and requires huge memory consumption. Local learning, which updates each layer independently with a gradient-isolated auxiliary network, offers a promising alternative to address the above problems. However, existing local learning methods are confronted with a large accuracy gap with the BP counterpart, particularly for large-scale networks. This is due to the weak coupling between local layers and their subsequent network layers, as there is no gradient communication across layers. To tackle this issue, we put forward an augmented local learning method, dubbed AugLocal. AugLocal constructs each hidden layer's auxiliary network by uniformly selecting a small subset of layers from its subsequent network layers to enhance their synergy. We also propose to linearly reduce the depth of auxiliary networks as the hidden layer goes deeper, ensuring sufficient network capacity while reducing the computational cost of auxiliary networks. Our extensive experiments on four image classification datasets (i.e., CIFAR-10, SVHN, STL-10, and ImageNet) demonstrate that AugLocal can effectively scale up to tens of local layers with a comparable accuracy to BP-trained networks while reducing GPU memory usage by around 40%. The proposed AugLocal method, therefore, opens up a myriad of opportunities for training high-performance deep neural networks on resource-constrained platforms.Code is available at https://github.com/ChenxiangMA/AugLocal.
|
[
"['Chenxiang Ma' 'Jibin Wu' 'Chenyang Si' 'Kay Chen Tan']"
] |
null | null |
2402.17327
| null | null |
http://arxiv.org/pdf/2402.17327v1
|
2024-02-27T09:03:43Z
|
2024-02-27T09:03:43Z
|
Data-Efficient Learning via Clustering-Based Sensitivity Sampling:
Foundation Models and Beyond
|
We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model. We present a new data selection approach based on $k$-means clustering and sensitivity sampling. Assuming access to an embedding representation of the data with respect to which the model loss is H"older continuous, our approach provably allows selecting a set of ``typical'' $k + 1/varepsilon^2$ elements whose average loss corresponds to the average loss of the whole dataset, up to a multiplicative $(1pmvarepsilon)$ factor and an additive $varepsilon lambda Phi_k$, where $Phi_k$ represents the $k$-means cost for the input embeddings and $lambda$ is the H"older constant. We furthermore demonstrate the performance and scalability of our approach on fine-tuning foundation models and show that it outperforms state-of-the-art methods. We also show how it can be applied on linear regression, leading to a new sampling strategy that surprisingly matches the performances of leverage score sampling, while being conceptually simpler and more scalable.
|
[
"['Kyriakos Axiotis' 'Vincent Cohen-Addad' 'Monika Henzinger'\n 'Sammy Jerome' 'Vahab Mirrokni' 'David Saulpic' 'David Woodruff'\n 'Michael Wunder']"
] |
null | null |
2402.17336
| null | null |
http://arxiv.org/pdf/2402.17336v1
|
2024-02-27T09:11:10Z
|
2024-02-27T09:11:10Z
|
Outdoor Environment Reconstruction with Deep Learning on Radio
Propagation Paths
|
Conventional methods for outdoor environment reconstruction rely predominantly on vision-based techniques like photogrammetry and LiDAR, facing limitations such as constrained coverage, susceptibility to environmental conditions, and high computational and energy demands. These challenges are particularly pronounced in applications like augmented reality navigation, especially when integrated with wearable devices featuring constrained computational resources and energy budgets. In response, this paper proposes a novel approach harnessing ambient wireless signals for outdoor environment reconstruction. By analyzing radio frequency (RF) data, the paper aims to deduce the environmental characteristics and digitally reconstruct the outdoor surroundings. Investigating the efficacy of selected deep learning (DL) techniques on the synthetic RF dataset WAIR-D, the study endeavors to address the research gap in this domain. Two DL-driven approaches are evaluated (convolutional U-Net and CLIP+ based on vision transformers), with performance assessed using metrics like intersection-over-union (IoU), Hausdorff distance, and Chamfer distance. The results demonstrate promising performance of the RF-based reconstruction method, paving the way towards lightweight and scalable reconstruction solutions.
|
[
"['Hrant Khachatrian' 'Rafayel Mkrtchyan' 'Theofanis P. Raptis']"
] |
null | null |
2402.17343
| null | null |
http://arxiv.org/pdf/2402.17343v1
|
2024-02-27T09:23:13Z
|
2024-02-27T09:23:13Z
|
Enhanced Bayesian Optimization via Preferential Modeling of Abstract
Properties
|
Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive and black-box experimental design processes. While Bayesian optimization is a principled data-driven approach to experimental optimization, it learns everything from scratch and could greatly benefit from the expertise of its human (domain) experts who often reason about systems at different abstraction levels using physical properties that are not necessarily directly measured (or measurable). In this paper, we propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into the surrogate modeling to further boost the performance of BO. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments. We discuss the convergence behavior of our proposed framework. Our experimental results involving synthetic functions and real-world datasets show the superiority of our method against the baselines.
|
[
"['Arun Kumar A V' 'Alistair Shilton' 'Sunil Gupta' 'Santu Rana'\n 'Stewart Greenhill' 'Svetha Venkatesh']"
] |
null | null |
2402.17345
| null | null |
http://arxiv.org/pdf/2402.17345v1
|
2024-02-27T09:23:54Z
|
2024-02-27T09:23:54Z
|
LocalGCL: Local-aware Contrastive Learning for Graphs
|
Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings. Meanwhile, the time-consuming and costly process of annotating graph labels manually prompts the growth of self-supervised learning (SSL) techniques. As a dominant approach of SSL, Contrastive learning (CL) learns discriminative representations by differentiating between positive and negative samples. However, when applied to graph data, it overemphasizes global patterns while neglecting local structures. To tackle the above issue, we propose underline{Local}-aware underline{G}raph underline{C}ontrastive underline{L}earning (textbf{methnametrim}), a self-supervised learning framework that supplementarily captures local graph information with masking-based modeling compared with vanilla contrastive learning. Extensive experiments validate the superiority of methname against state-of-the-art methods, demonstrating its promise as a comprehensive graph representation learner.
|
[
"['Haojun Jiang' 'Jiawei Sun' 'Jie Li' 'Chentao Wu']"
] |
null | null |
2402.17346
| null | null |
http://arxiv.org/pdf/2402.17346v1
|
2024-02-27T09:27:54Z
|
2024-02-27T09:27:54Z
|
Understanding the training of PINNs for unsteady flow past a plunging
foil through the lens of input subdomain level loss function gradients
|
Recently immersed boundary method-inspired physics-informed neural networks (PINNs) including the moving boundary-enabled PINNs (MB-PINNs) have shown the ability to accurately reconstruct velocity and recover pressure as a hidden variable for unsteady flow past moving bodies. Considering flow past a plunging foil, MB-PINNs were trained with global physics loss relaxation and also in conjunction with a physics-based undersampling method, obtaining good accuracy. The purpose of this study was to investigate which input spatial subdomain contributes to the training under the effect of physics loss relaxation and physics-based undersampling. In the context of MB-PINNs training, three spatial zones: the moving body, wake, and outer zones were defined. To quantify which spatial zone drives the training, two novel metrics are computed from the zonal loss component gradient statistics and the proportion of sample points in each zone. Results confirm that the learning indeed depends on the combined effect of the zonal loss component gradients and the proportion of points in each zone. Moreover, the dominant input zones are also the ones that have the strongest solution gradients in some sense.
|
[
"['Rahul Sundar' 'Didier Lucor' 'Sunetra Sarkar']"
] |
null | null |
2402.17363
| null | null |
http://arxiv.org/pdf/2402.17363v1
|
2024-02-27T09:55:34Z
|
2024-02-27T09:55:34Z
|
CGGM: A conditional graph generation model with adaptive sparsity for
node anomaly detection in IoT networks
|
Dynamic graphs are extensively employed for detecting anomalous behavior in nodes within the Internet of Things (IoT). Generative models are often used to address the issue of imbalanced node categories in dynamic graphs. Nevertheless, the constraints it faces include the monotonicity of adjacency relationships, the difficulty in constructing multi-dimensional features for nodes, and the lack of a method for end-to-end generation of multiple categories of nodes. This paper presents a novel graph generation model, called CGGM, designed specifically to generate a larger number of nodes belonging to the minority class. The mechanism for generating an adjacency matrix, through adaptive sparsity, enhances flexibility in its structure. The feature generation module, called multidimensional features generator (MFG) to generate node features along with topological information. Labels are transformed into embedding vectors, serving as conditional constraints to control the generation of synthetic data across multiple categories. Using a multi-stage loss, the distribution of synthetic data is adjusted to closely resemble that of real data. In extensive experiments, we show that CGGM's synthetic data outperforms state-of-the-art methods across various metrics. Our results demonstrate efficient generation of diverse data categories, robustly enhancing multi-category classification model performance.
|
[
"['Xianshi Su' 'Munan Li' 'Tongbang Jiang' 'Hao Long']"
] |
null | null |
2402.17375
| null | null |
http://arxiv.org/pdf/2402.17375v1
|
2024-02-27T10:12:47Z
|
2024-02-27T10:12:47Z
|
Impact of Computation in Integral Reinforcement Learning for
Continuous-Time Control
|
Integral reinforcement learning (IntRL) demands the precise computation of the utility function's integral at its policy evaluation (PEV) stage. This is achieved through quadrature rules, which are weighted sums of utility functions evaluated from state samples obtained in discrete time. Our research reveals a critical yet underexplored phenomenon: the choice of the computational method -- in this case, the quadrature rule -- can significantly impact control performance. This impact is traced back to the fact that computational errors introduced in the PEV stage can affect the policy iteration's convergence behavior, which in turn affects the learned controller. To elucidate how computation impacts control, we draw a parallel between IntRL's policy iteration and Newton's method applied to the Hamilton-Jacobi-Bellman equation. In this light, computational error in PEV manifests as an extra error term in each iteration of Newton's method, with its upper bound proportional to the computational error. Further, we demonstrate that when the utility function resides in a reproducing kernel Hilbert space (RKHS), the optimal quadrature is achievable by employing Bayesian quadrature with the RKHS-inducing kernel function. We prove that the local convergence rates for IntRL using the trapezoidal rule and Bayesian quadrature with a Mat'ern kernel to be $O(N^{-2})$ and $O(N^{-b})$, where $N$ is the number of evenly-spaced samples and $b$ is the Mat'ern kernel's smoothness parameter. These theoretical findings are finally validated by two canonical control tasks.
|
[
"['Wenhan Cao' 'Wei Pan']"
] |
null | null |
2402.17376
| null | null |
http://arxiv.org/pdf/2402.17376v3
|
2024-07-03T06:16:31Z
|
2024-02-27T10:13:30Z
|
Accelerating Diffusion Sampling with Optimized Time Steps
|
Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis, but their sampling efficiency is still to be desired due to the typically large number of sampling steps. Recent advancements in high-order numerical ODE solvers for DPMs have enabled the generation of high-quality images with much fewer sampling steps. While this is a significant development, most sampling methods still employ uniform time steps, which is not optimal when using a small number of steps. To address this issue, we propose a general framework for designing an optimization problem that seeks more appropriate time steps for a specific numerical ODE solver for DPMs. This optimization problem aims to minimize the distance between the ground-truth solution to the ODE and an approximate solution corresponding to the numerical solver. It can be efficiently solved using the constrained trust region method, taking less than $15$ seconds. Our extensive experiments on both unconditional and conditional sampling using pixel- and latent-space DPMs demonstrate that, when combined with the state-of-the-art sampling method UniPC, our optimized time steps significantly improve image generation performance in terms of FID scores for datasets such as CIFAR-10 and ImageNet, compared to using uniform time steps.
|
[
"['Shuchen Xue' 'Zhaoqiang Liu' 'Fei Chen' 'Shifeng Zhang' 'Tianyang Hu'\n 'Enze Xie' 'Zhenguo Li']"
] |
null | null |
2402.17390
| null | null |
http://arxiv.org/pdf/2402.17390v1
|
2024-02-27T10:37:13Z
|
2024-02-27T10:37:13Z
|
Robustness-Congruent Adversarial Training for Secure Machine Learning
Model Updates
|
Machine-learning models demand for periodic updates to improve their average accuracy, exploiting novel architectures and additional data. However, a newly-updated model may commit mistakes that the previous model did not make. Such misclassifications are referred to as negative flips, and experienced by users as a regression of performance. In this work, we show that this problem also affects robustness to adversarial examples, thereby hindering the development of secure model update practices. In particular, when updating a model to improve its adversarial robustness, some previously-ineffective adversarial examples may become misclassified, causing a regression in the perceived security of the system. We propose a novel technique, named robustness-congruent adversarial training, to address this issue. It amounts to fine-tuning a model with adversarial training, while constraining it to retain higher robustness on the adversarial examples that were correctly classified before the update. We show that our algorithm and, more generally, learning with non-regression constraints, provides a theoretically-grounded framework to train consistent estimators. Our experiments on robust models for computer vision confirm that (i) both accuracy and robustness, even if improved after model update, can be affected by negative flips, and (ii) our robustness-congruent adversarial training can mitigate the problem, outperforming competing baseline methods.
|
[
"['Daniele Angioni' 'Luca Demetrio' 'Maura Pintor' 'Luca Oneto'\n 'Davide Anguita' 'Battista Biggio' 'Fabio Roli']"
] |
null | null |
2402.17398
| null | null |
http://arxiv.org/pdf/2402.17398v3
|
2024-07-04T10:06:23Z
|
2024-02-27T10:46:36Z
|
A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)
|
The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE, inspired by the Synthetic Minority Oversampling Technique (SMOTE), generates synthetic data points using quantum processes such as swap tests and quantum rotation. The process varies from the conventional SMOTE algorithm's usage of K-Nearest Neighbors (KNN) and Euclidean distances, enabling synthetic instances to be generated from minority class data points without relying on neighbor proximity. The algorithm asserts greater control over the synthetic data generation process by introducing hyperparameters such as rotation angle, minority percentage, and splitting factor, which allow for customization to specific dataset requirements. Due to the use of a compact swap test, the algorithm can accommodate a large number of features. Furthermore, the approach is tested on a public dataset of Telecom Churn and evaluated alongside two prominent classification algorithms, Random Forest and Logistic Regression, to determine its impact along with varying proportions of synthetic data.
|
[
"['Nishikanta Mohanty' 'Bikash K. Behera' 'Christopher Ferrie'\n 'Pravat Dash']"
] |
null | null |
2402.17402
| null | null |
http://arxiv.org/pdf/2402.17402v2
|
2024-04-18T08:58:27Z
|
2024-02-27T10:48:56Z
|
Beacon, a lightweight deep reinforcement learning benchmark library for
flow control
|
Recently, the increasing use of deep reinforcement learning for flow control problems has led to a new area of research, focused on the coupling and the adaptation of the existing algorithms to the control of numerical fluid dynamics environments. Although still in its infancy, the field has seen multiple successes in a short time span, and its fast development pace can certainly be partly imparted to the open-source effort that drives the expansion of the community. Yet, this emerging domain still misses a common ground to (i) ensure the reproducibility of the results, and (ii) offer a proper ad-hoc benchmarking basis. To this end, we propose Beacon, an open-source benchmark library composed of seven lightweight 1D and 2D flow control problems with various characteristics, action and observation space characteristics, and CPU requirements. In this contribution, the seven considered problems are described, and reference control solutions are provided. The sources for the following work are available at https://github.com/jviquerat/beacon.
|
[
"['Jonathan Viquerat' 'Philippe Meliga' 'Pablo Jeken' 'Elie Hachem']"
] |
null | null |
2402.17406
| null | null |
http://arxiv.org/pdf/2402.17406v1
|
2024-02-27T10:55:07Z
|
2024-02-27T10:55:07Z
|
LSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning
|
Visual Prompt Tuning (VPT) techniques have gained prominence for their capacity to adapt pre-trained Vision Transformers (ViTs) to downstream visual tasks using specialized learnable tokens termed as prompts. Contemporary VPT methodologies, especially when employed with self-supervised vision transformers, often default to the introduction of new learnable prompts or gated prompt tokens predominantly sourced from the model's previous block. A pivotal oversight in such approaches is their failure to harness the potential of long-range previous blocks as sources of prompts within each self-supervised ViT. To bridge this crucial gap, we introduce Long-term Spatial Prompt Tuning (LSPT) - a revolutionary approach to visual representation learning. Drawing inspiration from the intricacies of the human brain, LSPT ingeniously incorporates long-term gated prompts. This feature serves as temporal coding, curbing the risk of forgetting parameters acquired from earlier blocks. Further enhancing its prowess, LSPT brings into play patch tokens, serving as spatial coding. This is strategically designed to perpetually amass class-conscious features, thereby fortifying the model's prowess in distinguishing and identifying visual categories. To validate the efficacy of our proposed method, we engaged in rigorous experimentation across 5 FGVC and 19 VTAB-1K benchmarks. Our empirical findings underscore the superiority of LSPT, showcasing its ability to set new benchmarks in visual prompt tuning performance.
|
[
"['Shentong Mo' 'Yansen Wang' 'Xufang Luo' 'Dongsheng Li']"
] |
null | null |
2402.17410
| null | null |
http://arxiv.org/pdf/2402.17410v1
|
2024-02-27T11:01:58Z
|
2024-02-27T11:01:58Z
|
A novel image space formalism of Fourier domain interpolation neural
networks for noise propagation analysis
|
Purpose: To develop an image space formalism of multi-layer convolutional neural networks (CNNs) for Fourier domain interpolation in MRI reconstructions and analytically estimate noise propagation during CNN inference. Theory and Methods: Nonlinear activations in the Fourier domain (also known as k-space) using complex-valued Rectifier Linear Units are expressed as elementwise multiplication with activation masks. This operation is transformed into a convolution in the image space. After network training in k-space, this approach provides an algebraic expression for the derivative of the reconstructed image with respect to the aliased coil images, which serve as the input tensors to the network in the image space. This allows the variance in the network inference to be estimated analytically and to be used to describe noise characteristics. Monte-Carlo simulations and numerical approaches based on auto-differentiation were used for validation. The framework was tested on retrospectively undersampled invivo brain images. Results: Inferences conducted in the image domain are quasi-identical to inferences in the k-space, underlined by corresponding quantitative metrics. Noise variance maps obtained from the analytical expression correspond with those obtained via Monte-Carlo simulations, as well as via an auto-differentiation approach. The noise resilience is well characterized, as in the case of classical Parallel Imaging. Komolgorov-Smirnov tests demonstrate Gaussian distributions of voxel magnitudes in variance maps obtained via Monte-Carlo simulations. Conclusion: The quasi-equivalent image space formalism for neural networks for k-space interpolation enables fast and accurate description of the noise characteristics during CNN inference, analogous to geometry-factor maps in traditional parallel imaging methods.
|
[
"['Peter Dawood' 'Felix Breuer' 'Istvan Homolya' 'Jannik Stebani'\n 'Maximilian Gram' 'Peter M. Jakob' 'Moritz Zaiss' 'Martin Blaimer']"
] |
null | null |
2402.17423
| null | null |
http://arxiv.org/pdf/2402.17423v2
|
2024-07-04T05:41:44Z
|
2024-02-27T11:32:14Z
|
Reinforced In-Context Black-Box Optimization
|
Black-Box Optimization (BBO) has found successful applications in many fields of science and engineering. Recently, there has been a growing interest in meta-learning particular components of BBO algorithms to speed up optimization and get rid of tedious hand-crafted heuristics. As an extension, learning the entire algorithm from data requires the least labor from experts and can provide the most flexibility. In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion. RIBBO employs expressive sequence models to learn the optimization histories produced by multiple behavior algorithms and tasks, leveraging the in-context learning ability of large models to extract task information and make decisions accordingly. Central to our method is to augment the optimization histories with textit{regret-to-go} tokens, which are designed to represent the performance of an algorithm based on cumulative regret over the future part of the histories. The integration of regret-to-go tokens enables RIBBO to automatically generate sequences of query points that satisfy the user-desired regret, which is verified by its universally good empirical performance on diverse problems, including BBO benchmark functions, hyper-parameter optimization and robot control problems.
|
[
"['Lei Song' 'Chenxiao Gao' 'Ke Xue' 'Chenyang Wu' 'Dong Li' 'Jianye Hao'\n 'Zongzhang Zhang' 'Chao Qian']"
] |
null | null |
2402.17431
| null | null |
http://arxiv.org/pdf/2402.17431v1
|
2024-02-27T11:43:41Z
|
2024-02-27T11:43:41Z
|
The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning
with Kandinsky Patterns
|
Artificial intelligence is continuously seeking novel challenges and benchmarks to effectively measure performance and to advance the state-of-the-art. In this paper we introduce KANDY, a benchmarking framework that can be used to generate a variety of learning and reasoning tasks inspired by Kandinsky patterns. By creating curricula of binary classification tasks with increasing complexity and with sparse supervisions, KANDY can be used to implement benchmarks for continual and semi-supervised learning, with a specific focus on symbol compositionality. Classification rules are also provided in the ground truth to enable analysis of interpretable solutions. Together with the benchmark generation pipeline, we release two curricula, an easier and a harder one, that we propose as new challenges for the research community. With a thorough experimental evaluation, we show how both state-of-the-art neural models and purely symbolic approaches struggle with solving most of the tasks, thus calling for the application of advanced neuro-symbolic methods trained over time.
|
[
"['Luca Salvatore Lorello' 'Marco Lippi' 'Stefano Melacci']"
] |
null | null |
2402.17440
| null | null |
http://arxiv.org/pdf/2402.17440v1
|
2024-02-27T11:52:49Z
|
2024-02-27T11:52:49Z
|
Principled Architecture-aware Scaling of Hyperparameters
|
Training a high-quality deep neural network requires choosing suitable hyperparameters, which is a non-trivial and expensive process. Current works try to automatically optimize or design principles of hyperparameters, such that they can generalize to diverse unseen scenarios. However, most designs or optimization methods are agnostic to the choice of network structures, and thus largely ignore the impact of neural architectures on hyperparameters. In this work, we precisely characterize the dependence of initializations and maximal learning rates on the network architecture, which includes the network depth, width, convolutional kernel size, and connectivity patterns. By pursuing every parameter to be maximally updated with the same mean squared change in pre-activations, we can generalize our initialization and learning rates across MLPs (multi-layer perception) and CNNs (convolutional neural network) with sophisticated graph topologies. We verify our principles with comprehensive experiments. More importantly, our strategy further sheds light on advancing current benchmarks for architecture design. A fair comparison of AutoML algorithms requires accurate network rankings. However, we demonstrate that network rankings can be easily changed by better training networks in benchmarks with our architecture-aware learning rates and initialization.
|
[
"['Wuyang Chen' 'Junru Wu' 'Zhangyang Wang' 'Boris Hanin']"
] |
null | null |
2402.17453
| null | null |
http://arxiv.org/pdf/2402.17453v5
|
2024-05-28T06:50:38Z
|
2024-02-27T12:26:07Z
|
DS-Agent: Automated Data Science by Empowering Large Language Models
with Case-Based Reasoning
|
In this work, we investigate the potential of large language models (LLMs) based agents to automate data science tasks, with the goal of comprehending task requirements, then building and training the best-fit machine learning models. Despite their widespread success, existing LLM agents are hindered by generating unreasonable experiment plans within this scenario. To this end, we present DS-Agent, a novel automatic framework that harnesses LLM agent and case-based reasoning (CBR). In the development stage, DS-Agent follows the CBR framework to structure an automatic iteration pipeline, which can flexibly capitalize on the expert knowledge from Kaggle, and facilitate consistent performance improvement through the feedback mechanism. Moreover, DS-Agent implements a low-resource deployment stage with a simplified CBR paradigm to adapt past successful solutions from the development stage for direct code generation, significantly reducing the demand on foundational capabilities of LLMs. Empirically, DS-Agent with GPT-4 achieves 100% success rate in the development stage, while attaining 36% improvement on average one pass rate across alternative LLMs in the deployment stage. In both stages, DS-Agent achieves the best rank in performance, costing $1.60 and $0.13 per run with GPT-4, respectively. Our data and code are open-sourced at https://github.com/guosyjlu/DS-Agent.
|
[
"['Siyuan Guo' 'Cheng Deng' 'Ying Wen' 'Hechang Chen' 'Yi Chang' 'Jun Wang']"
] |
null | null |
2402.17457
| null | null |
http://arxiv.org/pdf/2402.17457v1
|
2024-02-27T12:28:01Z
|
2024-02-27T12:28:01Z
|
Why do Learning Rates Transfer? Reconciling Optimization and Scaling
Limits for Deep Learning
|
Recently, there has been growing evidence that if the width and depth of a neural network are scaled toward the so-called rich feature learning limit ($mu$P and its depth extension), then some hyperparameters - such as the learning rate - exhibit transfer from small to very large models, thus reducing the cost of hyperparameter tuning. From an optimization perspective, this phenomenon is puzzling, as it implies that the loss landscape is remarkably consistent across very different model sizes. In this work, we find empirical evidence that learning rate transfer can be attributed to the fact that under $mu$P and its depth extension, the largest eigenvalue of the training loss Hessian (i.e. the sharpness) is largely independent of the width and depth of the network for a sustained period of training time. On the other hand, we show that under the neural tangent kernel (NTK) regime, the sharpness exhibits very different dynamics at different scales, thus preventing learning rate transfer. But what causes these differences in the sharpness dynamics? Through a connection between the spectra of the Hessian and the NTK matrix, we argue that the cause lies in the presence (for $mu$P) or progressive absence (for the NTK regime) of feature learning, which results in a different evolution of the NTK, and thus of the sharpness. We corroborate our claims with a substantial suite of experiments, covering a wide range of datasets and architectures: from ResNets and Vision Transformers trained on benchmark vision datasets to Transformers-based language models trained on WikiText
|
[
"['Lorenzo Noci' 'Alexandru Meterez' 'Thomas Hofmann' 'Antonio Orvieto']"
] |
null | null |
2402.17470
| null | null |
http://arxiv.org/pdf/2402.17470v1
|
2024-02-27T12:52:44Z
|
2024-02-27T12:52:44Z
|
Bit Distribution Study and Implementation of Spatial Quality Map in the
JPEG-AI Standardization
|
Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate faster coding speeds on devices compared to the hand-crafted transforms used in classical frameworks. The scientific and industrial communities are highly interested in these properties, leading to the standardization effort of JPEG-AI. The JPEG-AI verification model has been released and is currently under development for standardization. Utilizing neural networks, it can outperform the classic codec VVC intra by over 10% BD-rate operating at base operation point. Researchers attribute this success to the flexible bit distribution in the spatial domain, in contrast to VVC intra's anchor that is generated with a constant quality point. However, our study reveals that VVC intra displays a more adaptable bit distribution structure through the implementation of various block sizes. As a result of our observations, we have proposed a spatial bit allocation method to optimize the JPEG-AI verification model's bit distribution and enhance the visual quality. Furthermore, by applying the VVC bit distribution strategy, the objective performance of JPEG-AI verification mode can be further improved, resulting in a maximum gain of 0.45 dB in PSNR-Y.
|
[
"['Panqi Jia' 'Jue Mao' 'Esin Koyuncu' 'A. Burakhan Koyuncu'\n 'Timofey Solovyev' 'Alexander Karabutov' 'Yin Zhao' 'Elena Alshina'\n 'Andre Kaup']"
] |
null | null |
2402.17472
| null | null |
http://arxiv.org/pdf/2402.17472v3
|
2024-05-18T14:23:09Z
|
2024-02-27T12:53:15Z
|
RAGFormer: Learning Semantic Attributes and Topological Structure for
Fraud Detection
|
Fraud detection remains a challenging task due to the complex and deceptive nature of fraudulent activities. Current approaches primarily concentrate on learning only one perspective of the graph: either the topological structure of the graph or the attributes of individual nodes. However, we conduct empirical studies to reveal that these two types of features, while nearly orthogonal, are each independently effective. As a result, previous methods can not fully capture the comprehensive characteristics of the fraud graph. To address this dilemma, we present a novel framework called Relation-Aware GNN with transFormer~(RAGFormer) which simultaneously embeds both semantic and topological features into a target node. The simple yet effective network consists of a semantic encoder, a topology encoder, and an attention fusion module. The semantic encoder utilizes Transformer to learn semantic features and node interactions across different relations. We introduce Relation-Aware GNN as the topology encoder to learn topological features and node interactions within each relation. These two complementary features are interleaved through an attention fusion module to support prediction by both orthogonal features. Extensive experiments on two popular public datasets demonstrate that RAGFormer achieves state-of-the-art performance. The significant improvement of RAGFormer in an industrial credit card fraud detection dataset further validates the applicability of our method in real-world business scenarios.
|
[
"['Haolin Li' 'Shuyang Jiang' 'Lifeng Zhang' 'Siyuan Du' 'Guangnan Ye'\n 'Hongfeng Chai']"
] |
null | null |
2402.17487
| null | null |
http://arxiv.org/pdf/2402.17487v1
|
2024-02-27T13:12:18Z
|
2024-02-27T13:12:18Z
|
Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
|
The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in the classical frameworks, NN-based models learn the non-linear transforms providing more compact bit representations, and achieve faster coding speed on parallel devices over their classical counterparts. Those properties evoked the attention of both scientific and industrial communities, resulting in the standardization activity JPEG-AI. The verification model for the standardization process of JPEG-AI is already in development and has surpassed the advanced VVC intra codec. To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed. However, the current state of the JPEG-AI verification model experiences significant slowdowns during bit rate matching, resulting in suboptimal performance due to an unsuitable model. The proposed methodology offers a gradual algorithmic optimization for matching bit rates, resulting in a fourfold acceleration and over 1% improvement in BD-rate at the base operation point. At the high operation point, the acceleration increases up to sixfold.
|
[
"['Panqi Jia' 'A. Burakhan Koyuncu' 'Jue Mao' 'Ze Cui' 'Yi Ma'\n 'Tiansheng Guo' 'Timofey Solovyev' 'Alexander Karabutov' 'Yin Zhao'\n 'Jing Wang' 'Elena Alshina' 'Andre Kaup']"
] |
null | null |
2402.17492
| null | null |
http://arxiv.org/abs/2402.17492v2
|
2024-04-15T08:12:04Z
|
2024-02-27T13:18:00Z
|
syren-halofit: A fast, interpretable, high-precision formula for the
$Λ$CDM nonlinear matter power spectrum
|
Rapid and accurate evaluation of the nonlinear matter power spectrum, $P(k)$, as a function of cosmological parameters and redshift is of fundamental importance in cosmology. Analytic approximations provide an interpretable solution, yet current approximations are neither fast nor accurate relative to numerical emulators. We use symbolic regression to obtain simple analytic approximations to the nonlinear scale, $k_sigma$, the effective spectral index, $n_{rm eff}$, and the curvature, $C$, which are required for the halofit model. We then re-optimise the coefficients of halofit to fit a wide range of cosmologies and redshifts. We explore the space of analytic expressions to fit the residuals between $P(k)$ and the optimised predictions of halofit. Our results are designed to match the predictions of EuclidEmulator2, but are validated against $N$-body simulations. Our symbolic expressions for $k_sigma$, $n_{rm eff}$ and $C$ have root mean squared fractional errors of 0.8%, 0.2% and 0.3%, respectively, for redshifts below 3 and a wide range of cosmologies. The re-optimised halofit parameters reduce the root mean squared fractional error (compared to EuclidEmulator2) from 3% to below 2% for wavenumbers $k=9times10^{-3}-9 , h{rm Mpc^{-1}}$. We introduce syren-halofit (symbolic-regression-enhanced halofit), an extension to halofit containing a short symbolic correction which improves this error to 1%. Our method is 2350 and 3170 times faster than current halofit and hmcode implementations, respectively, and 2680 and 64 times faster than EuclidEmulator2 (which requires running class) and the BACCO emulator. We obtain comparable accuracy to EuclidEmulator2 and BACCO when tested on $N$-body simulations. Our work greatly increases the speed and accuracy of symbolic approximations to $P(k)$, making them significantly faster than their numerical counterparts without loss of accuracy.
|
[
"['Deaglan J. Bartlett' 'Benjamin D. Wandelt' 'Matteo Zennaro'\n 'Pedro G. Ferreira' 'Harry Desmond']"
] |
null | null |
2402.17500
| null | null |
http://arxiv.org/pdf/2402.17500v1
|
2024-02-27T13:34:08Z
|
2024-02-27T13:34:08Z
|
Predicting Instability in Complex Oscillator Networks: Limitations and
Potentials of Network Measures and Machine Learning
|
A central question of network science is how functional properties of systems arise from their structure. For networked dynamical systems, structure is typically quantified with network measures. A functional property that is of theoretical and practical interest for oscillatory systems is the stability of synchrony to localized perturbations. Recently, Graph Neural Networks (GNNs) have been shown to predict this stability successfully; at the same time, network measures have struggled to paint a clear picture. Here we collect 46 relevant network measures and find that no small subset can reliably predict stability. The performance of GNNs can only be matched by combining all network measures and nodewise machine learning. However, unlike GNNs, this approach fails to extrapolate from network ensembles to several real power grid topologies. This suggests that correlations of network measures and function may be misleading, and that GNNs capture the causal relationship between structure and stability substantially better.
|
[
"['Christian Nauck' 'Michael Lindner' 'Nora Molkenthin' 'Jürgen Kurths'\n 'Eckehard Schöll' 'Jörg Raisch' 'Frank Hellmann']"
] |
null | null |
2402.17501
| null | null |
http://arxiv.org/pdf/2402.17501v2
|
2024-05-24T18:50:06Z
|
2024-02-27T13:36:55Z
|
Intensive Care as One Big Sequence Modeling Problem
|
Reinforcement Learning in Healthcare is typically concerned with narrow self-contained tasks such as sepsis prediction or anesthesia control. However, previous research has demonstrated the potential of generalist models (the prime example being Large Language Models) to outperform task-specific approaches due to their capability for implicit transfer learning. To enable training of foundation models for Healthcare as well as leverage the capabilities of state of the art Transformer architectures, we propose the paradigm of Healthcare as Sequence Modeling, in which interaction between the patient and the healthcare provider is represented as an event stream and tasks like diagnosis and treatment selection are modeled as prediction of future events in the stream. To explore this paradigm experimentally we develop MIMIC-SEQ, a sequence modeling benchmark derived by translating heterogenous clinical records from MIMIC-IV dataset into a uniform event stream format, train a baseline model and explore its capabilities.
|
[
"['Vadim Liventsev' 'Tobias Fritz']"
] |
null | null |
2402.17506
| null | null |
http://arxiv.org/pdf/2402.17506v2
|
2024-07-03T12:13:21Z
|
2024-02-27T13:46:45Z
|
Thermodynamics-informed super-resolution of scarce temporal dynamics
data
|
We present a method to increase the resolution of measurements of a physical system and subsequently predict its time evolution using thermodynamics-aware neural networks. Our method uses adversarial autoencoders, which reduce the dimensionality of the full order model to a set of latent variables that are enforced to match a prior, for example a normal distribution. Adversarial autoencoders are seen as generative models, and they can be trained to generate high-resolution samples from low-resoution inputs, meaning they can address the so-called super-resolution problem. Then, a second neural network is trained to learn the physical structure of the latent variables and predict their temporal evolution. This neural network is known as an structure-preserving neural network. It learns the metriplectic-structure of the system and applies a physical bias to ensure that the first and second principles of thermodynamics are fulfilled. The integrated trajectories are decoded to their original dimensionality, as well as to the higher dimensionality space produced by the adversarial autoencoder and they are compared to the ground truth solution. The method is tested with two examples of flow over a cylinder, where the fluid properties are varied between both examples.
|
[
"['Carlos Bermejo-Barbanoj' 'Beatriz Moya' 'Alberto Badías'\n 'Francisco Chinesta' 'Elías Cueto']"
] |
null | null |
2402.17516
| null | null |
http://arxiv.org/pdf/2402.17516v3
|
2024-04-29T19:57:16Z
|
2024-02-27T14:00:08Z
|
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for
Generative Counterfactual Explanations
|
Deep Neural Networks (DNNs) stand out as one of the most prominent approaches within the Machine Learning (ML) domain. The efficacy of DNNs has surged alongside recent increases in computational capacity, allowing these approaches to scale to significant complexities for addressing predictive challenges in big data. However, as the complexity of DNN models rises, interpretability diminishes. In response to this challenge, explainable models such as Adversarial Gradient Integration (AGI) leverage path-based gradients provided by DNNs to elucidate their decisions. Yet the performance of path-based explainers can be compromised when gradients exhibit irregularities during out-of-distribution path traversal. In this context, we introduce Quantified Uncertainty Counterfactual Explanations (QUCE), a method designed to mitigate out-of-distribution traversal by minimizing path uncertainty. QUCE not only quantifies uncertainty when presenting explanations but also generates more certain counterfactual examples. We showcase the performance of the QUCE method by comparing it with competing methods for both path-based explanations and generative counterfactual examples.
|
[
"['Jamie Duell' 'Monika Seisenberger' 'Hsuan Fu' 'Xiuyi Fan']"
] |
null | null |
2402.17517
| null | null |
http://arxiv.org/pdf/2402.17517v1
|
2024-02-27T14:00:34Z
|
2024-02-27T14:00:34Z
|
Label-Noise Robust Diffusion Models
|
Conditional diffusion models have shown remarkable performance in various generative tasks, but training them requires large-scale datasets that often contain noise in conditional inputs, a.k.a. noisy labels. This noise leads to condition mismatch and quality degradation of generated data. This paper proposes Transition-aware weighted Denoising Score Matching (TDSM) for training conditional diffusion models with noisy labels, which is the first study in the line of diffusion models. The TDSM objective contains a weighted sum of score networks, incorporating instance-wise and time-dependent label transition probabilities. We introduce a transition-aware weight estimator, which leverages a time-dependent noisy-label classifier distinctively customized to the diffusion process. Through experiments across various datasets and noisy label settings, TDSM improves the quality of generated samples aligned with given conditions. Furthermore, our method improves generation performance even on prevalent benchmark datasets, which implies the potential noisy labels and their risk of generative model learning. Finally, we show the improved performance of TDSM on top of conventional noisy label corrections, which empirically proving its contribution as a part of label-noise robust generative models. Our code is available at: https://github.com/byeonghu-na/tdsm.
|
[
"['Byeonghu Na' 'Yeongmin Kim' 'HeeSun Bae' 'Jung Hyun Lee' 'Se Jung Kwon'\n 'Wanmo Kang' 'Il-Chul Moon']"
] |
null | null |
2402.17544
| null | null |
http://arxiv.org/pdf/2402.17544v1
|
2024-02-27T14:34:14Z
|
2024-02-27T14:34:14Z
|
Adapting Learned Image Codecs to Screen Content via Adjustable
Transformations
|
As learned image codecs (LICs) become more prevalent, their low coding efficiency for out-of-distribution data becomes a bottleneck for some applications. To improve the performance of LICs for screen content (SC) images without breaking backwards compatibility, we propose to introduce parameterized and invertible linear transformations into the coding pipeline without changing the underlying baseline codec's operation flow. We design two neural networks to act as prefilters and postfilters in our setup to increase the coding efficiency and help with the recovery from coding artifacts. Our end-to-end trained solution achieves up to 10% bitrate savings on SC compression compared to the baseline LICs while introducing only 1% extra parameters.
|
[
"['H. Burak Dogaroglu' 'A. Burakhan Koyuncu' 'Atanas Boev' 'Elena Alshina'\n 'Eckehard Steinbach']"
] |
null | null |
2402.17554
| null | null |
http://arxiv.org/pdf/2402.17554v1
|
2024-02-27T14:48:07Z
|
2024-02-27T14:48:07Z
|
Evaluation of Predictive Reliability to Foster Trust in Artificial
Intelligence. A case study in Multiple Sclerosis
|
Applying Artificial Intelligence (AI) and Machine Learning (ML) in critical contexts, such as medicine, requires the implementation of safety measures to reduce risks of harm in case of prediction errors. Spotting ML failures is of paramount importance when ML predictions are used to drive clinical decisions. ML predictive reliability measures the degree of trust of a ML prediction on a new instance, thus allowing decision-makers to accept or reject it based on its reliability. To assess reliability, we propose a method that implements two principles. First, our approach evaluates whether an instance to be classified is coming from the same distribution of the training set. To do this, we leverage Autoencoders (AEs) ability to reconstruct the training set with low error. An instance is considered Out-of-Distribution (OOD) if the AE reconstructs it with a high error. Second, it is evaluated whether the ML classifier has good performances on samples similar to the newly classified instance by using a proxy model. We show that this approach is able to assess reliability both in a simulated scenario and on a model trained to predict disease progression of Multiple Sclerosis patients. We also developed a Python package, named relAI, to embed reliability measures into ML pipelines. We propose a simple approach that can be used in the deployment phase of any ML model to suggest whether to trust predictions or not. Our method holds the promise to provide effective support to clinicians by spotting potential ML failures during deployment.
|
[
"['Lorenzo Peracchio' 'Giovanna Nicora' 'Enea Parimbelli'\n 'Tommaso Mario Buonocore' 'Roberto Bergamaschi' 'Eleonora Tavazzi'\n 'Arianna Dagliati' 'Riccardo Bellazzi']"
] |
null | null |
2402.17563
| null | null |
http://arxiv.org/pdf/2402.17563v2
|
2024-03-04T14:51:40Z
|
2024-02-27T15:05:13Z
|
Structure-Guided Adversarial Training of Diffusion Models
|
Diffusion models have demonstrated exceptional efficacy in various generative applications. While existing models focus on minimizing a weighted sum of denoising score matching losses for data distribution modeling, their training primarily emphasizes instance-level optimization, overlooking valuable structural information within each mini-batch, indicative of pair-wise relationships among samples. To address this limitation, we introduce Structure-guided Adversarial training of Diffusion Models (SADM). In this pioneering approach, we compel the model to learn manifold structures between samples in each training batch. To ensure the model captures authentic manifold structures in the data distribution, we advocate adversarial training of the diffusion generator against a novel structure discriminator in a minimax game, distinguishing real manifold structures from the generated ones. SADM substantially improves existing diffusion transformers (DiT) and outperforms existing methods in image generation and cross-domain fine-tuning tasks across 12 datasets, establishing a new state-of-the-art FID of 1.58 and 2.11 on ImageNet for class-conditional image generation at resolutions of 256x256 and 512x512, respectively.
|
[
"['Ling Yang' 'Haotian Qian' 'Zhilong Zhang' 'Jingwei Liu' 'Bin Cui']"
] |
null | null |
2402.17570
| null | null |
http://arxiv.org/pdf/2402.17570v3
|
2024-07-02T17:25:19Z
|
2024-02-27T15:08:57Z
|
Sparse Variational Contaminated Noise Gaussian Process Regression with
Applications in Geomagnetic Perturbations Forecasting
|
Gaussian Processes (GP) have become popular machine-learning methods for kernel-based learning on datasets with complicated covariance structures. In this paper, we present a novel extension to the GP framework using a contaminated normal likelihood function to better account for heteroscedastic variance and outlier noise. We propose a scalable inference algorithm based on the Sparse Variational Gaussian Process (SVGP) method for fitting sparse Gaussian process regression models with contaminated normal noise on large datasets. We examine an application to geomagnetic ground perturbations, where the state-of-the-art prediction model is based on neural networks. We show that our approach yields shorter prediction intervals for similar coverage and accuracy when compared to an artificial dense neural network baseline.
|
[
"['Daniel Iong' 'Matthew McAnear' 'Yuezhou Qu' 'Shasha Zou' 'Gabor Toth'\n 'Yang Chen']"
] |
null | null |
2402.17572
| null | null |
http://arxiv.org/pdf/2402.17572v1
|
2024-02-27T15:09:20Z
|
2024-02-27T15:09:20Z
|
Hyperdimensional computing: a fast, robust and interpretable paradigm
for biological data
|
Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzing biological sequences, deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. However, these methods are incredibly data-hungry, compute-intensive and hard to interpret. Hyperdimensional computing (HDC) has recently emerged as an intriguing alternative. The key idea is that random vectors of high dimensionality can represent concepts such as sequence identity or phylogeny. These vectors can then be combined using simple operators for learning, reasoning or querying by exploiting the peculiar properties of high-dimensional spaces. Our work reviews and explores the potential of HDC for bioinformatics, emphasizing its efficiency, interpretability, and adeptness in handling multimodal and structured data. HDC holds a lot of potential for various omics data searching, biosignal analysis and health applications.
|
[
"['Michiel Stock' 'Dimitri Boeckaerts' 'Pieter Dewulf' 'Steff Taelman'\n 'Maxime Van Haeverbeke' 'Wim Van Criekinge' 'Bernard De Baets']"
] |
null | null |
2402.17583
| null | null |
http://arxiv.org/abs/2402.17583v1
|
2024-02-27T15:14:19Z
|
2024-02-27T15:14:19Z
|
FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in
Large-scale Cloud Systems
|
Postmortem analysis is essential in the management of incidents within cloud systems, which provides valuable insights to improve system's reliability and robustness. At CloudA, fault pattern profiling is performed during the postmortem phase, which involves the classification of incidents' faults into unique categories, referred to as fault pattern. By aggregating and analyzing these fault patterns, engineers can discern common faults, vulnerable components and emerging fault trends. However, this process is currently conducted by manual labeling, which has inherent drawbacks. On the one hand, the sheer volume of incidents means only the most severe ones are analyzed, causing a skewed overview of fault patterns. On the other hand, the complexity of the task demands extensive domain knowledge, which leads to errors and inconsistencies. To address these limitations, we propose an automated approach, named FaultProfIT, for Fault pattern Profiling of Incident Tickets. It leverages hierarchy-guided contrastive learning to train a hierarchy-aware incident encoder and predicts fault patterns with enhanced incident representations. We evaluate FaultProfIT using the production incidents from CloudA. The results demonstrate that FaultProfIT outperforms state-of-the-art methods. Our ablation study and analysis also verify the effectiveness of hierarchy-guided contrastive learning. Additionally, we have deployed FaultProfIT at CloudA for six months. To date, FaultProfIT has analyzed 10,000+ incidents from 30+ cloud services, successfully revealing several fault trends that have informed system improvements.
|
[
"['Junjie Huang' 'Jinyang Liu' 'Zhuangbin Chen' 'Zhihan Jiang' 'Yichen LI'\n 'Jiazhen Gu' 'Cong Feng' 'Zengyin Yang' 'Yongqiang Yang' 'Michael R. Lyu']"
] |
null | null |
2402.17595
| null | null |
http://arxiv.org/pdf/2402.17595v1
|
2024-02-27T15:28:01Z
|
2024-02-27T15:28:01Z
|
Implicit Regularization via Spectral Neural Networks and Non-linear
Matrix Sensing
|
The phenomenon of implicit regularization has attracted interest in recent years as a fundamental aspect of the remarkable generalizing ability of neural networks. In a nutshell, it entails that gradient descent dynamics in many neural nets, even without any explicit regularizer in the loss function, converges to the solution of a regularized learning problem. However, known results attempting to theoretically explain this phenomenon focus overwhelmingly on the setting of linear neural nets, and the simplicity of the linear structure is particularly crucial to existing arguments. In this paper, we explore this problem in the context of more realistic neural networks with a general class of non-linear activation functions, and rigorously demonstrate the implicit regularization phenomenon for such networks in the setting of matrix sensing problems, together with rigorous rate guarantees that ensure exponentially fast convergence of gradient descent.In this vein, we contribute a network architecture called Spectral Neural Networks (abbrv. SNN) that is particularly suitable for matrix learning problems. Conceptually, this entails coordinatizing the space of matrices by their singular values and singular vectors, as opposed to by their entries, a potentially fruitful perspective for matrix learning. We demonstrate that the SNN architecture is inherently much more amenable to theoretical analysis than vanilla neural nets and confirm its effectiveness in the context of matrix sensing, via both mathematical guarantees and empirical investigations. We believe that the SNN architecture has the potential to be of wide applicability in a broad class of matrix learning scenarios.
|
[
"['Hong T. M. Chu' 'Subhro Ghosh' 'Chi Thanh Lam'\n 'Soumendu Sundar Mukherjee']"
] |
null | null |
2402.17599
| null | null |
http://arxiv.org/pdf/2402.17599v2
|
2024-02-28T10:46:07Z
|
2024-02-26T11:29:16Z
|
DAGnosis: Localized Identification of Data Inconsistencies using
Structures
|
Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models. While recent data-centric methods are able to identify such inconsistencies with respect to the training set, they suffer from two key limitations: (1) suboptimality in settings where features exhibit statistical independencies, due to their usage of compressive representations and (2) lack of localization to pin-point why a sample might be flagged as inconsistent, which is important to guide future data collection. We solve these two fundamental limitations using directed acyclic graphs (DAGs) to encode the training set's features probability distribution and independencies as a structure. Our method, called DAGnosis, leverages these structural interactions to bring valuable and insightful data-centric conclusions. DAGnosis unlocks the localization of the causes of inconsistencies on a DAG, an aspect overlooked by previous approaches. Moreover, we show empirically that leveraging these interactions (1) leads to more accurate conclusions in detecting inconsistencies, as well as (2) provides more detailed insights into why some samples are flagged.
|
[
"['Nicolas Huynh' 'Jeroen Berrevoets' 'Nabeel Seedat' 'Jonathan Crabbé'\n 'Zhaozhi Qian' 'Mihaela van der Schaar']"
] |
null | null |
2402.17601
| null | null |
http://arxiv.org/pdf/2402.17601v1
|
2024-02-27T15:30:01Z
|
2024-02-27T15:30:01Z
|
Advancing sleep detection by modelling weak label sets: A novel weakly
supervised learning approach
|
Understanding sleep and activity patterns plays a crucial role in physical and mental health. This study introduces a novel approach for sleep detection using weakly supervised learning for scenarios where reliable ground truth labels are unavailable. The proposed method relies on a set of weak labels, derived from the predictions generated by conventional sleep detection algorithms. Introducing a novel approach, we suggest a novel generalised non-linear statistical model in which the number of weak sleep labels is modelled as outcome of a binomial distribution. The probability of sleep in the binomial distribution is linked to the outcomes of neural networks trained to detect sleep based on actigraphy. We show that maximizing the likelihood function of the model, is equivalent to minimizing the soft cross-entropy loss. Additionally, we explored the use of the Brier score as a loss function for weak labels. The efficacy of the suggested modelling framework was demonstrated using the Multi-Ethnic Study of Atherosclerosis dataset. A gls{lstm} trained on the soft cross-entropy outperformed conventional sleep detection algorithms, other neural network architectures and loss functions in accuracy and model calibration. This research not only advances sleep detection techniques in scenarios where ground truth data is scarce but also contributes to the broader field of weakly supervised learning by introducing innovative approach in modelling sets of weak labels.
|
[
"['Matthias Boeker' 'Vajira Thambawita' 'Michael Riegler' 'Pål Halvorsen'\n 'Hugo L. Hammer']"
] |
null | null |
2402.17606
| null | null |
http://arxiv.org/pdf/2402.17606v3
|
2024-06-05T06:19:06Z
|
2024-02-27T15:33:20Z
|
Learning Topological Representations with Bidirectional Graph Attention
Network for Solving Job Shop Scheduling Problem
|
Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs). This paper proposes the topology-aware bidirectional graph attention network (TBGAT), a novel GNN architecture based on the attention mechanism, to embed the DG for solving JSSP in a local search framework. Specifically, TBGAT embeds the DG from a forward and a backward view, respectively, where the messages are propagated by following the different topologies of the views and aggregated via graph attention. Then, we propose a novel operator based on the message-passing mechanism to calculate the forward and backward topological sorts of the DG, which are the features for characterizing the topological structures and exploited by our model. In addition, we theoretically and experimentally show that TBGAT has linear computational complexity to the number of jobs and machines, respectively, strengthening our method's practical value. Besides, extensive experiments on five synthetic datasets and seven classic benchmarks show that TBGAT achieves new SOTA results by outperforming a wide range of neural methods by a large margin. All the code and data are publicly available online at https://github.com/zcaicaros/TBGAT.
|
[
"['Cong Zhang' 'Zhiguang Cao' 'Yaoxin Wu' 'Wen Song' 'Jing Sun']"
] |
null | null |
2402.17621
| null | null |
http://arxiv.org/pdf/2402.17621v1
|
2024-02-27T15:49:26Z
|
2024-02-27T15:49:26Z
|
Supervised machine learning for microbiomics: bridging the gap between
current and best practices
|
Machine learning (ML) is set to accelerate innovations in clinical microbiomics, such as in disease diagnostics and prognostics. This will require high-quality, reproducible, interpretable workflows whose predictive capabilities meet or exceed the high thresholds set for clinical tools by regulatory agencies. Here, we capture a snapshot of current practices in the application of supervised ML to microbiomics data, through an in-depth analysis of 100 peer-reviewed journal articles published in 2021-2022. We apply a data-driven approach to steer discussion of the merits of varied approaches to experimental design, including key considerations such as how to mitigate the effects of small dataset size while avoiding data leakage. We further provide guidance on how to avoid common experimental design pitfalls that can hurt model performance, trustworthiness, and reproducibility. Discussion is accompanied by an interactive online tutorial that demonstrates foundational principles of ML experimental design, tailored to the microbiomics community. Formalizing community best practices for supervised ML in microbiomics is an important step towards improving the success and efficiency of clinical research, to the benefit of patients and other stakeholders.
|
[
"['Natasha K. Dudek' 'Mariam Chakhvadze' 'Saba Kobakhidze' 'Omar Kantidze'\n 'Yuriy Gankin']"
] |
null | null |
2402.17641
| null | null |
http://arxiv.org/pdf/2402.17641v2
|
2024-06-06T04:31:43Z
|
2024-02-27T16:11:05Z
|
Variational Learning is Effective for Large Deep Networks
|
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variational Online Newton (IVON) consistently matches or outperforms Adam for training large networks such as GPT-2 and ResNets from scratch. IVON's computational costs are nearly identical to Adam but its predictive uncertainty is better. We show several new use cases of IVON where we improve finetuning and model merging in Large Language Models, accurately predict generalization error, and faithfully estimate sensitivity to data. We find overwhelming evidence that variational learning is effective.
|
[
"['Yuesong Shen' 'Nico Daheim' 'Bai Cong' 'Peter Nickl'\n 'Gian Maria Marconi' 'Clement Bazan' 'Rio Yokota' 'Iryna Gurevych'\n 'Daniel Cremers' 'Mohammad Emtiyaz Khan' 'Thomas Möllenhoff']"
] |
null | null |
2402.17655
| null | null |
http://arxiv.org/pdf/2402.17655v2
|
2024-05-21T16:22:20Z
|
2024-02-27T16:24:28Z
|
Confidence-Aware Multi-Field Model Calibration
|
Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding. However, there often exist unwanted mismatches between predicted probabilities and true likelihoods due to the rapid shift of data distributions and intrinsic model biases. Calibration aims to address this issue by post-processing model predictions, and field-aware calibration can adjust model output on different feature field values to satisfy fine-grained advertising demands. Unfortunately, the observed samples corresponding to certain field values can be seriously limited to make confident calibrations, which may yield bias amplification and online disturbance. In this paper, we propose a confidence-aware multi-field calibration method, which adaptively adjusts the calibration intensity based on confidence levels derived from sample statistics. It also utilizes multiple fields for joint model calibration according to their importance to mitigate the impact of data sparsity on a single field. Extensive offline and online experiments show the superiority of our method in boosting advertising performance and reducing prediction deviations.
|
[
"['Yuang Zhao' 'Chuhan Wu' 'Qinglin Jia' 'Hong Zhu' 'Jia Yan' 'Libin Zong'\n 'Linxuan Zhang' 'Zhenhua Dong' 'Muyu Zhang']"
] |
null | null |
2402.17660
| null | null |
http://arxiv.org/abs/2402.17660v3
|
2024-05-23T14:22:55Z
|
2024-02-27T16:27:06Z
|
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular
Simulations
|
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2-fold to 10-fold over previous iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and the smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.
|
[
"['Raul P. Pelaez' 'Guillem Simeon' 'Raimondas Galvelis' 'Antonio Mirarchi'\n 'Peter Eastman' 'Stefan Doerr' 'Philipp Thölke' 'Thomas E. Markland'\n 'Gianni De Fabritiis']"
] |
null | null |
2402.17666
| null | null |
http://arxiv.org/pdf/2402.17666v1
|
2024-02-27T16:36:53Z
|
2024-02-27T16:36:53Z
|
Multi-Agent Deep Reinforcement Learning for Distributed Satellite
Routing
|
This paper introduces a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach for routing in Low Earth Orbit Satellite Constellations (LSatCs). Each satellite is an independent decision-making agent with a partial knowledge of the environment, and supported by feedback received from the nearby agents. Building on our previous work that introduced a Q-routing solution, the contribution of this paper is to extend it to a deep learning framework able to quickly adapt to the network and traffic changes, and based on two phases: (1) An offline exploration learning phase that relies on a global Deep Neural Network (DNN) to learn the optimal paths at each possible position and congestion level; (2) An online exploitation phase with local, on-board, pre-trained DNNs. Results show that MA-DRL efficiently learns optimal routes offline that are then loaded for an efficient distributed routing online.
|
[
"['Federico Lozano-Cuadra' 'Beatriz Soret']"
] |
null | null |
2402.17671
| null | null |
http://arxiv.org/pdf/2402.17671v1
|
2024-02-27T16:44:09Z
|
2024-02-27T16:44:09Z
|
Securing Reliability: A Brief Overview on Enhancing In-Context Learning
for Foundation Models
|
As foundation models (FMs) continue to shape the landscape of AI, the in-context learning (ICL) paradigm thrives but also encounters issues such as toxicity, hallucination, disparity, adversarial vulnerability, and inconsistency. Ensuring the reliability and responsibility of FMs is crucial for the sustainable development of the AI ecosystem. In this concise overview, we investigate recent advancements in enhancing the reliability and trustworthiness of FMs within ICL frameworks, focusing on four key methodologies, each with its corresponding subgoals. We sincerely hope this paper can provide valuable insights for researchers and practitioners endeavoring to build safe and dependable FMs and foster a stable and consistent ICL environment, thereby unlocking their vast potential.
|
[
"['Yunpeng Huang' 'Yaonan Gu' 'Jingwei Xu' 'Zhihong Zhu' 'Zhaorun Chen'\n 'Xiaoxing Ma']"
] |
null | null |
2402.17686
| null | null |
http://arxiv.org/pdf/2402.17686v1
|
2024-02-27T17:01:21Z
|
2024-02-27T17:01:21Z
|
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
|
Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods - Ensembles, Deep Evidential Regression (DER), and Gaussian Mixture Models (GMM) - were applied to the H-transfer reaction between ${it syn-}$Criegee and vinyl hydroxyperoxide. The results indicate that ensemble models provide the best results for detecting outliers, followed by GMM. For example, from a pool of 1000 structures with the largest uncertainty, the detection quality for outliers is $sim 90$ % and $sim 50$ %, respectively, if 25 or 1000 structures with large errors are sought. On the contrary, the limitations of the statistical assumptions of DER greatly impacted its prediction capabilities. Finally, a structure-based indicator was found to be correlated with large average error, which may help to rapidly classify new structures into those that provide an advantage for refining the neural network.
|
[
"['Luis Itza Vazquez-Salazar' 'Silvan Käser' 'Markus Meuwly']"
] |
null | null |
2402.17689
| null | null |
http://arxiv.org/pdf/2402.17689v1
|
2024-02-27T17:05:41Z
|
2024-02-27T17:05:41Z
|
QoS prediction in radio vehicular environments via prior user
information
|
Reliable wireless communications play an important role in the automotive industry as it helps to enhance current use cases and enable new ones such as connected autonomous driving, platooning, cooperative maneuvering, teleoperated driving, and smart navigation. These and other use cases often rely on specific quality of service (QoS) levels for communication. Recently, the area of predictive quality of service (QoS) has received a great deal of attention as a key enabler to forecast communication quality well enough in advance. However, predicting QoS in a reliable manner is a notoriously difficult task. In this paper, we evaluate ML tree-ensemble methods to predict QoS in the range of minutes with data collected from a cellular test network. We discuss radio environment characteristics and we showcase how these can be used to improve ML performance and further support the uptake of ML in commercial networks. Specifically, we use the correlations of the measurements coming from the radio environment by including information of prior vehicles to enhance the prediction of the target vehicles. Moreover, we are extending prior art by showing how longer prediction horizons can be supported.
|
[
"['Noor Ul Ain' 'Rodrigo Hernangómez' 'Alexandros Palaios'\n 'Martin Kasparick' 'Sławomir Stańczak']"
] |
null | null |
2402.17690
| null | null |
http://arxiv.org/pdf/2402.17690v2
|
2024-02-28T15:53:07Z
|
2024-02-27T17:07:18Z
|
Autonomous Vehicles: Evolution of Artificial Intelligence and Learning
Algorithms
|
The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of Artificial Intelligence (AI) and learning algorithms, propelling vehicles into realms of unprecedented autonomy. This paper provides a comprehensive exploration of the evolutionary trajectory of AI within autonomous vehicles, tracing the journey from foundational principles to the most recent advancements. Commencing with a current landscape overview, the paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing ethical considerations and bias in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI/learning algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI and learning algorithms, and automating key tasks at each level. Additionally, the document discusses the variation in software package sizes across different autonomy levels
|
[
"['Divya Garikapati' 'Sneha Sudhir Shetiya']"
] |
null | null |
2402.17695
| null | null |
http://arxiv.org/pdf/2402.17695v1
|
2024-02-27T17:11:35Z
|
2024-02-27T17:11:35Z
|
Geometric Deep Learning for Computer-Aided Design: A Survey
|
Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and engineers approach and enhance the design process. By harnessing the power of machine learning-based methods, CAD designers can optimize their workflows, save time and effort while making better informed decisions, and create designs that are both innovative and practical. The ability to process the CAD designs represented by geometric data and to analyze their encoded features enables the identification of similarities among diverse CAD models, the proposition of alternative designs and enhancements, and even the generation of novel design alternatives. This survey offers a comprehensive overview of learning-based methods in computer-aided design across various categories, including similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds. Additionally, it provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain. The final discussion delves into the challenges prevalent in this field, followed by potential future research directions in this rapidly evolving field.
|
[
"['Negar Heidari' 'Alexandros Iosifidis']"
] |
null | null |
2402.17698
| null | null |
http://arxiv.org/pdf/2402.17698v1
|
2024-02-27T17:21:10Z
|
2024-02-27T17:21:10Z
|
Learning reduced-order Quadratic-Linear models in Process Engineering
using Operator Inference
|
In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This is a non-intrusive, data-driven method for learning dynamical systems from time-domain data. The application in our study is carbon dioxide methanation, an important reaction within the Power-to-X framework, to demonstrate its potential. The numerical results show the ability of the reduced-order models constructed with operator inference to provide a reduced yet accurate surrogate solution. This represents an important milestone towards the implementation of fast and reliable digital twin architectures.
|
[
"['Ion Victor Gosea' 'Luisa Peterson' 'Pawan Goyal' 'Jens Bremer'\n 'Kai Sundmacher' 'Peter Benner']"
] |
null | null |
2402.17699
| null | null |
http://arxiv.org/pdf/2402.17699v1
|
2024-02-27T17:23:40Z
|
2024-02-27T17:23:40Z
|
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
|
Discrete distributions, particularly in high-dimensional deep models, are often highly multimodal due to inherent discontinuities. While gradient-based discrete sampling has proven effective, it is susceptible to becoming trapped in local modes due to the gradient information. To tackle this challenge, we propose an automatic cyclical scheduling, designed for efficient and accurate sampling in multimodal discrete distributions. Our method contains three key components: (1) a cyclical step size schedule where large steps discover new modes and small steps exploit each mode; (2) a cyclical balancing schedule, ensuring ``balanced" proposals for given step sizes and high efficiency of the Markov chain; and (3) an automatic tuning scheme for adjusting the hyperparameters in the cyclical schedules, allowing adaptability across diverse datasets with minimal tuning. We prove the non-asymptotic convergence and inference guarantee for our method in general discrete distributions. Extensive experiments demonstrate the superiority of our method in sampling complex multimodal discrete distributions.
|
[
"['Patrick Pynadath' 'Riddhiman Bhattacharya' 'Arun Hariharan' 'Ruqi Zhang']"
] |
null | null |
2402.17700
| null | null |
http://arxiv.org/pdf/2402.17700v1
|
2024-02-27T17:25:37Z
|
2024-02-27T17:25:37Z
|
RAVEL: Evaluating Interpretability Methods on Disentangling Language
Model Representations
|
Individual neurons participate in the representation of multiple high-level concepts. To what extent can different interpretability methods successfully disentangle these roles? To help address this question, we introduce RAVEL (Resolving Attribute-Value Entanglements in Language Models), a dataset that enables tightly controlled, quantitative comparisons between a variety of existing interpretability methods. We use the resulting conceptual framework to define the new method of Multi-task Distributed Alignment Search (MDAS), which allows us to find distributed representations satisfying multiple causal criteria. With Llama2-7B as the target language model, MDAS achieves state-of-the-art results on RAVEL, demonstrating the importance of going beyond neuron-level analyses to identify features distributed across activations. We release our benchmark at https://github.com/explanare/ravel.
|
[
"['Jing Huang' 'Zhengxuan Wu' 'Christopher Potts' 'Mor Geva'\n 'Atticus Geiger']"
] |
null | null |
2402.17701
| null | null |
http://arxiv.org/pdf/2402.17701v1
|
2024-02-27T17:26:33Z
|
2024-02-27T17:26:33Z
|
Real-time Low-latency Music Source Separation using Hybrid
Spectrogram-TasNet
|
There have been significant advances in deep learning for music demixing in recent years. However, there has been little attention given to how these neural networks can be adapted for real-time low-latency applications, which could be helpful for hearing aids, remixing audio streams and live shows. In this paper, we investigate the various challenges involved in adapting current demixing models in the literature for this use case. Subsequently, inspired by the Hybrid Demucs architecture, we propose the Hybrid Spectrogram Time-domain Audio Separation Network HS-TasNet, which utilises the advantages of spectral and waveform domains. For a latency of 23 ms, the HS-TasNet obtains an overall signal-to-distortion ratio (SDR) of 4.65 on the MusDB test set, and increases to 5.55 with additional training data. These results demonstrate the potential of efficient demixing for real-time low-latency music applications.
|
[
"['Satvik Venkatesh' 'Arthur Benilov' 'Philip Coleman' 'Frederic Roskam']"
] |
null | null |
2402.17704
| null | null |
http://arxiv.org/pdf/2402.17704v1
|
2024-02-27T17:30:33Z
|
2024-02-27T17:30:33Z
|
Transfer Learning Bayesian Optimization to Design Competitor DNA
Molecules for Use in Diagnostic Assays
|
With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of experiments workflow to make this development feasible. By combining a transfer learning surrogate model with Bayesian optimization, we show how the total number of experiments can be reduced by sharing information between optimization tasks. We demonstrate the reduction in the number of experiments using data from the development of DNA competitors for use in an amplification-based diagnostic assay. We use cross-validation to compare the predictive accuracy of different transfer learning models, and then compare the performance of the models for both single objective and penalized optimization tasks.
|
[
"['Ruby Sedgwick' 'John P. Goertz' 'Molly M. Stevens' 'Ruth Misener'\n 'Mark van der Wilk']"
] |
null | null |
2402.17705
| null | null |
http://arxiv.org/pdf/2402.17705v2
|
2024-06-24T04:21:33Z
|
2024-02-27T17:33:23Z
|
Federated Learning for Estimating Heterogeneous Treatment Effects
|
Machine learning methods for estimating heterogeneous treatment effects (HTE) facilitate large-scale personalized decision-making across various domains such as healthcare, policy making, education, and more. Current machine learning approaches for HTE require access to substantial amounts of data per treatment, and the high costs associated with interventions makes centrally collecting so much data for each intervention a formidable challenge. To overcome this obstacle, in this work, we propose a novel framework for collaborative learning of HTE estimators across institutions via Federated Learning. We show that even under a diversity of interventions and subject populations across clients, one can jointly learn a common feature representation, while concurrently and privately learning the specific predictive functions for outcomes under distinct interventions across institutions. Our framework and the associated algorithm are based on this insight, and leverage tabular transformers to map multiple input data to feature representations which are then used for outcome prediction via multi-task learning. We also propose a novel way of federated training of personalised transformers that can work with heterogeneous input feature spaces. Experimental results on real-world clinical trial data demonstrate the effectiveness of our method.
|
[
"['Disha Makhija' 'Joydeep Ghosh' 'Yejin Kim']"
] |
null | null |
2402.17710
| null | null |
http://arxiv.org/pdf/2402.17710v1
|
2024-02-27T17:43:51Z
|
2024-02-27T17:43:51Z
|
Understanding Neural Network Binarization with Forward and Backward
Proximal Quantizers
|
In neural network binarization, BinaryConnect (BC) and its variants are considered the standard. These methods apply the sign function in their forward pass and their respective gradients are backpropagated to update the weights. However, the derivative of the sign function is zero whenever defined, which consequently freezes training. Therefore, implementations of BC (e.g., BNN) usually replace the derivative of sign in the backward computation with identity or other approximate gradient alternatives. Although such practice works well empirically, it is largely a heuristic or ''training trick.'' We aim at shedding some light on these training tricks from the optimization perspective. Building from existing theory on ProxConnect (PC, a generalization of BC), we (1) equip PC with different forward-backward quantizers and obtain ProxConnect++ (PC++) that includes existing binarization techniques as special cases; (2) derive a principled way to synthesize forward-backward quantizers with automatic theoretical guarantees; (3) illustrate our theory by proposing an enhanced binarization algorithm BNN++; (4) conduct image classification experiments on CNNs and vision transformers, and empirically verify that BNN++ generally achieves competitive results on binarizing these models.
|
[
"['Yiwei Lu' 'Yaoliang Yu' 'Xinlin Li' 'Vahid Partovi Nia']"
] |
null | null |
2402.17718
| null | null |
http://arxiv.org/pdf/2402.17718v1
|
2024-02-27T17:53:13Z
|
2024-02-27T17:53:13Z
|
Towards a Digital Twin Framework in Additive Manufacturing: Machine
Learning and Bayesian Optimization for Time Series Process Optimization
|
Laser-directed-energy deposition (DED) offers advantages in additive manufacturing (AM) for creating intricate geometries and material grading. Yet, challenges like material inconsistency and part variability remain, mainly due to its layer-wise fabrication. A key issue is heat accumulation during DED, which affects the material microstructure and properties. While closed-loop control methods for heat management are common in DED research, few integrate real-time monitoring, physics-based modeling, and control in a unified framework. Our work presents a digital twin (DT) framework for real-time predictive control of DED process parameters to meet specific design objectives. We develop a surrogate model using Long Short-Term Memory (LSTM)-based machine learning with Bayesian Inference to predict temperatures in DED parts. This model predicts future temperature states in real time. We also introduce Bayesian Optimization (BO) for Time Series Process Optimization (BOTSPO), based on traditional BO but featuring a unique time series process profile generator with reduced dimensions. BOTSPO dynamically optimizes processes, identifying optimal laser power profiles to attain desired mechanical properties. The established process trajectory guides online optimizations, aiming to enhance performance. This paper outlines the digital twin framework's components, promoting its integration into a comprehensive system for AM.
|
[
"['Vispi Karkaria' 'Anthony Goeckner' 'Rujing Zha' 'Jie Chen'\n 'Jianjing Zhang' 'Qi Zhu' 'Jian Cao' 'Robert X. Gao' 'Wei Chen']"
] |
null | null |
2402.17720
| null | null |
http://arxiv.org/pdf/2402.17720v1
|
2024-02-27T17:55:33Z
|
2024-02-27T17:55:33Z
|
The SMART approach to instance-optimal online learning
|
We devise an online learning algorithm -- titled Switching via Monotone Adapted Regret Traces (SMART) -- that adapts to the data and achieves regret that is instance optimal, i.e., simultaneously competitive on every input sequence compared to the performance of the follow-the-leader (FTL) policy and the worst case guarantee of any other input policy. We show that the regret of the SMART policy on any input sequence is within a multiplicative factor $e/(e-1) approx 1.58$ of the smaller of: 1) the regret obtained by FTL on the sequence, and 2) the upper bound on regret guaranteed by the given worst-case policy. This implies a strictly stronger guarantee than typical `best-of-both-worlds' bounds as the guarantee holds for every input sequence regardless of how it is generated. SMART is simple to implement as it begins by playing FTL and switches at most once during the time horizon to the worst-case algorithm. Our approach and results follow from an operational reduction of instance optimal online learning to competitive analysis for the ski-rental problem. We complement our competitive ratio upper bounds with a fundamental lower bound showing that over all input sequences, no algorithm can get better than a $1.43$-fraction of the minimum regret achieved by FTL and the minimax-optimal policy. We also present a modification of SMART that combines FTL with a ``small-loss" algorithm to achieve instance optimality between the regret of FTL and the small loss regret bound.
|
[
"['Siddhartha Banerjee' 'Alankrita Bhatt' 'Christina Lee Yu']"
] |
null | null |
2402.17722
| null | null |
http://arxiv.org/pdf/2402.17722v1
|
2024-02-27T17:56:49Z
|
2024-02-27T17:56:49Z
|
Taming Nonconvex Stochastic Mirror Descent with General Bregman
Divergence
|
This paper revisits the convergence of Stochastic Mirror Descent (SMD) in the contemporary nonconvex optimization setting. Existing results for batch-free nonconvex SMD restrict the choice of the distance generating function (DGF) to be differentiable with Lipschitz continuous gradients, thereby excluding important setups such as Shannon entropy. In this work, we present a new convergence analysis of nonconvex SMD supporting general DGF, that overcomes the above limitations and relies solely on the standard assumptions. Moreover, our convergence is established with respect to the Bregman Forward-Backward envelope, which is a stronger measure than the commonly used squared norm of gradient mapping. We further extend our results to guarantee high probability convergence under sub-Gaussian noise and global convergence under the generalized Bregman Proximal Polyak-{L}ojasiewicz condition. Additionally, we illustrate the advantages of our improved SMD theory in various nonconvex machine learning tasks by harnessing nonsmooth DGFs. Notably, in the context of nonconvex differentially private (DP) learning, our theory yields a simple algorithm with a (nearly) dimension-independent utility bound. For the problem of training linear neural networks, we develop provably convergent stochastic algorithms.
|
[
"['Ilyas Fatkhullin' 'Niao He']"
] |
null | null |
2402.17730
| null | null |
http://arxiv.org/pdf/2402.17730v1
|
2024-02-27T18:04:59Z
|
2024-02-27T18:04:59Z
|
Markovletics: Methods and A Novel Application for Learning
Continuous-Time Markov Chain Mixtures
|
Sequential data naturally arises from user engagement on digital platforms like social media, music streaming services, and web navigation, encapsulating evolving user preferences and behaviors through continuous information streams. A notable unresolved query in stochastic processes is learning mixtures of continuous-time Markov chains (CTMCs). While there is progress in learning mixtures of discrete-time Markov chains with recovery guarantees [GKV16,ST23,KTT2023], the continuous scenario uncovers unique unexplored challenges. The intrigue in CTMC mixtures stems from their potential to model intricate continuous-time stochastic processes prevalent in various fields including social media, finance, and biology. In this study, we introduce a novel framework for exploring CTMCs, emphasizing the influence of observed trails' length and mixture parameters on problem regimes, which demands specific algorithms. Through thorough experimentation, we examine the impact of discretizing continuous-time trails on the learnability of the continuous-time mixture, given that these processes are often observed via discrete, resource-demanding observations. Our comparative analysis with leading methods explores sample complexity and the trade-off between the number of trails and their lengths, offering crucial insights for method selection in different problem instances. We apply our algorithms on an extensive collection of Lastfm's user-generated trails spanning three years, demonstrating the capability of our algorithms to differentiate diverse user preferences. We pioneer the use of CTMC mixtures on a basketball passing dataset to unveil intricate offensive tactics of NBA teams. This underscores the pragmatic utility and versatility of our proposed framework. All results presented in this study are replicable, and we provide the implementations to facilitate reproducibility.
|
[
"['Fabian Spaeh' 'Charalampos E. Tsourakakis']"
] |
null | null |
2402.17732
| null | null |
http://arxiv.org/pdf/2402.17732v2
|
2024-06-10T21:10:00Z
|
2024-02-27T18:06:20Z
|
Batched Nonparametric Contextual Bandits
|
We study nonparametric contextual bandits under batch constraints, where the expected reward for each action is modeled as a smooth function of covariates, and the policy updates are made at the end of each batch of observations. We establish a minimax regret lower bound for this setting and propose a novel batch learning algorithm that achieves the optimal regret (up to logarithmic factors). In essence, our procedure dynamically splits the covariate space into smaller bins, carefully aligning their widths with the batch size. Our theoretical results suggest that for nonparametric contextual bandits, a nearly constant number of policy updates can attain optimal regret in the fully online setting.
|
[
"['Rong Jiang' 'Cong Ma']"
] |
null | null |
2402.17736
| null | null |
http://arxiv.org/pdf/2402.17736v2
|
2024-03-16T21:56:58Z
|
2024-02-27T18:12:58Z
|
Learning-Based Algorithms for Graph Searching Problems
|
We consider the problem of graph searching with prediction recently introduced by Banerjee et al. (2022). In this problem, an agent, starting at some vertex $r$ has to traverse a (potentially unknown) graph $G$ to find a hidden goal node $g$ while minimizing the total distance travelled. We study a setting in which at any node $v$, the agent receives a noisy estimate of the distance from $v$ to $g$. We design algorithms for this search task on unknown graphs. We establish the first formal guarantees on unknown weighted graphs and provide lower bounds showing that the algorithms we propose have optimal or nearly-optimal dependence on the prediction error. Further, we perform numerical experiments demonstrating that in addition to being robust to adversarial error, our algorithms perform well in typical instances in which the error is stochastic. Finally, we provide alternative simpler performance bounds on the algorithms of Banerjee et al. (2022) for the case of searching on a known graph, and establish new lower bounds for this setting.
|
[
"['Adela Frances DePavia' 'Erasmo Tani' 'Ali Vakilian']"
] |
null | null |
2402.17739
| null | null |
http://arxiv.org/pdf/2402.17739v2
|
2024-06-11T15:35:20Z
|
2024-02-27T18:18:23Z
|
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis
Use
|
The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals (SDG). In this work, we develop an online reinforcement learning (RL) algorithm called reBandit which will be utilized in a mobile health study to deliver personalized mobile health interventions aimed at reducing cannabis use among EAs. reBandit utilizes random effects and informative Bayesian priors to learn quickly and efficiently in noisy mobile health environments. Moreover, reBandit employs Empirical Bayes and optimization techniques to autonomously update its hyper-parameters online. To evaluate the performance of our algorithm, we construct a simulation testbed using data from a prior study, and compare against commonly used algorithms in mobile health studies. We show that reBandit performs equally well or better than all the baseline algorithms, and the performance gap widens as population heterogeneity increases in the simulation environment, proving its adeptness to adapt to diverse population of study participants.
|
[
"['Susobhan Ghosh' 'Yongyi Guo' 'Pei-Yao Hung' 'Lara Coughlin' 'Erin Bonar'\n 'Inbal Nahum-Shani' 'Maureen Walton' 'Susan Murphy']"
] |
null | null |
2402.17747
| null | null |
http://arxiv.org/pdf/2402.17747v3
|
2024-06-08T12:49:22Z
|
2024-02-27T18:32:11Z
|
When Your AIs Deceive You: Challenges of Partial Observability in
Reinforcement Learning from Human Feedback
|
Past analyses of reinforcement learning from human feedback (RLHF) assume that the human evaluators fully observe the environment. What happens when human feedback is based only on partial observations? We formally define two failure cases: deceptive inflation and overjustification. Modeling the human as Boltzmann-rational w.r.t. a belief over trajectories, we prove conditions under which RLHF is guaranteed to result in policies that deceptively inflate their performance, overjustify their behavior to make an impression, or both. Under the new assumption that the human's partial observability is known and accounted for, we then analyze how much information the feedback process provides about the return function. We show that sometimes, the human's feedback determines the return function uniquely up to an additive constant, but in other realistic cases, there is irreducible ambiguity. We propose exploratory research directions to help tackle these challenges and caution against blindly applying RLHF in partially observable settings.
|
[
"['Leon Lang' 'Davis Foote' 'Stuart Russell' 'Anca Dragan' 'Erik Jenner'\n 'Scott Emmons']"
] |
null | null |
2402.17750
| null | null |
http://arxiv.org/pdf/2402.17750v1
|
2024-02-27T18:37:22Z
|
2024-02-27T18:37:22Z
|
Scaling on-chip photonic neural processors using arbitrarily
programmable wave propagation
|
On-chip photonic processors for neural networks have potential benefits in both speed and energy efficiency but have not yet reached the scale at which they can outperform electronic processors. The dominant paradigm for designing on-chip photonics is to make networks of relatively bulky discrete components connected by one-dimensional waveguides. A far more compact alternative is to avoid explicitly defining any components and instead sculpt the continuous substrate of the photonic processor to directly perform the computation using waves freely propagating in two dimensions. We propose and demonstrate a device whose refractive index as a function of space, $n(x,z)$, can be rapidly reprogrammed, allowing arbitrary control over the wave propagation in the device. Our device, a 2D-programmable waveguide, combines photoconductive gain with the electro-optic effect to achieve massively parallel modulation of the refractive index of a slab waveguide, with an index modulation depth of $10^{-3}$ and approximately $10^4$ programmable degrees of freedom. We used a prototype device with a functional area of $12,text{mm}^2$ to perform neural-network inference with up to 49-dimensional input vectors in a single pass, achieving 96% accuracy on vowel classification and 86% accuracy on $7 times 7$-pixel MNIST handwritten-digit classification. This is a scale beyond that of previous photonic chips relying on discrete components, illustrating the benefit of the continuous-waves paradigm. In principle, with large enough chip area, the reprogrammability of the device's refractive index distribution enables the reconfigurable realization of any passive, linear photonic circuit or device. This promises the development of more compact and versatile photonic systems for a wide range of applications, including optical processing, smart sensing, spectroscopy, and optical communications.
|
[
"['Tatsuhiro Onodera' 'Martin M. Stein' 'Benjamin A. Ash'\n 'Mandar M. Sohoni' 'Melissa Bosch' 'Ryotatsu Yanagimoto' 'Marc Jankowski'\n 'Timothy P. McKenna' 'Tianyu Wang' 'Gennady Shvets'\n 'Maxim R. Shcherbakov' 'Logan G. Wright' 'Peter L. McMahon']"
] |
null | null |
2402.17753
| null | null |
http://arxiv.org/pdf/2402.17753v1
|
2024-02-27T18:42:31Z
|
2024-02-27T18:42:31Z
|
Evaluating Very Long-Term Conversational Memory of LLM Agents
|
Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in long-context large language models (LLMs) and retrieval augmented generation (RAG) techniques, their efficacy in very long-term dialogues remains unexplored. To address this research gap, we introduce a machine-human pipeline to generate high-quality, very long-term dialogues by leveraging LLM-based agent architectures and grounding their dialogues on personas and temporal event graphs. Moreover, we equip each agent with the capability of sharing and reacting to images. The generated conversations are verified and edited by human annotators for long-range consistency and grounding to the event graphs. Using this pipeline, we collect LoCoMo, a dataset of very long-term conversations, each encompassing 300 turns and 9K tokens on avg., over up to 35 sessions. Based on LoCoMo, we present a comprehensive evaluation benchmark to measure long-term memory in models, encompassing question answering, event summarization, and multi-modal dialogue generation tasks. Our experimental results indicate that LLMs exhibit challenges in understanding lengthy conversations and comprehending long-range temporal and causal dynamics within dialogues. Employing strategies like long-context LLMs or RAG can offer improvements but these models still substantially lag behind human performance.
|
[
"['Adyasha Maharana' 'Dong-Ho Lee' 'Sergey Tulyakov' 'Mohit Bansal'\n 'Francesco Barbieri' 'Yuwei Fang']"
] |
null | null |
2402.17756
| null | null |
http://arxiv.org/pdf/2402.17756v1
|
2024-02-27T18:48:07Z
|
2024-02-27T18:48:07Z
|
Robustly Learning Single-Index Models via Alignment Sharpness
|
We study the problem of learning Single-Index Models under the $L_2^2$ loss in the agnostic model. We give an efficient learning algorithm, achieving a constant factor approximation to the optimal loss, that succeeds under a range of distributions (including log-concave distributions) and a broad class of monotone and Lipschitz link functions. This is the first efficient constant factor approximate agnostic learner, even for Gaussian data and for any nontrivial class of link functions. Prior work for the case of unknown link function either works in the realizable setting or does not attain constant factor approximation. The main technical ingredient enabling our algorithm and analysis is a novel notion of a local error bound in optimization that we term alignment sharpness and that may be of broader interest.
|
[
"['Nikos Zarifis' 'Puqian Wang' 'Ilias Diakonikolas' 'Jelena Diakonikolas']"
] |
null | null |
2402.17760
| null | null |
http://arxiv.org/pdf/2402.17760v1
|
2024-02-27T18:53:18Z
|
2024-02-27T18:53:18Z
|
Learning to Program Variational Quantum Circuits with Fast Weights
|
Quantum Machine Learning (QML) has surfaced as a pioneering framework addressing sequential control tasks and time-series modeling. It has demonstrated empirical quantum advantages notably within domains such as Reinforcement Learning (RL) and time-series prediction. A significant advancement lies in Quantum Recurrent Neural Networks (QRNNs), specifically tailored for memory-intensive tasks encompassing partially observable environments and non-linear time-series prediction. Nevertheless, QRNN-based models encounter challenges, notably prolonged training duration stemming from the necessity to compute quantum gradients using backpropagation-through-time (BPTT). This predicament exacerbates when executing the complete model on quantum devices, primarily due to the substantial demand for circuit evaluation arising from the parameter-shift rule. This paper introduces the Quantum Fast Weight Programmers (QFWP) as a solution to the temporal or sequential learning challenge. The QFWP leverages a classical neural network (referred to as the 'slow programmer') functioning as a quantum programmer to swiftly modify the parameters of a variational quantum circuit (termed the 'fast programmer'). Instead of completely overwriting the fast programmer at each time-step, the slow programmer generates parameter changes or updates for the quantum circuit parameters. This approach enables the fast programmer to incorporate past observations or information. Notably, the proposed QFWP model achieves learning of temporal dependencies without necessitating the use of quantum recurrent neural networks. Numerical simulations conducted in this study showcase the efficacy of the proposed QFWP model in both time-series prediction and RL tasks. The model exhibits performance levels either comparable to or surpassing those achieved by QLSTM-based models.
|
[
"['Samuel Yen-Chi Chen']"
] |
null | null |
2402.17762
| null | null |
http://arxiv.org/pdf/2402.17762v1
|
2024-02-27T18:55:17Z
|
2024-02-27T18:55:17Z
|
Massive Activations in Large Language Models
|
We observe an empirical phenomenon in Large Language Models (LLMs) -- very few activations exhibit significantly larger values than others (e.g., 100,000 times larger). We call them massive activations. First, we demonstrate the widespread existence of massive activations across various LLMs and characterize their locations. Second, we find their values largely stay constant regardless of the input, and they function as indispensable bias terms in LLMs. Third, these massive activations lead to the concentration of attention probabilities to their corresponding tokens, and further, implicit bias terms in the self-attention output. Last, we also study massive activations in Vision Transformers.
|
[
"['Mingjie Sun' 'Xinlei Chen' 'J. Zico Kolter' 'Zhuang Liu']"
] |
null | null |
2402.17764
| null | null |
http://arxiv.org/pdf/2402.17764v1
|
2024-02-27T18:56:19Z
|
2024-02-27T18:56:19Z
|
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
|
Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.
|
[
"['Shuming Ma' 'Hongyu Wang' 'Lingxiao Ma' 'Lei Wang' 'Wenhui Wang'\n 'Shaohan Huang' 'Li Dong' 'Ruiping Wang' 'Jilong Xue' 'Furu Wei']"
] |
null | null |
2402.17767
| null | null |
http://arxiv.org/pdf/2402.17767v1
|
2024-02-27T18:58:54Z
|
2024-02-27T18:58:54Z
|
Opening Cabinets and Drawers in the Real World using a Commodity Mobile
Manipulator
|
Pulling open cabinets and drawers presents many difficult technical challenges in perception (inferring articulation parameters for objects from onboard sensors), planning (producing motion plans that conform to tight task constraints), and control (making and maintaining contact while applying forces on the environment). In this work, we build an end-to-end system that enables a commodity mobile manipulator (Stretch RE2) to pull open cabinets and drawers in diverse previously unseen real world environments. We conduct 4 days of real world testing of this system spanning 31 different objects from across 13 different real world environments. Our system achieves a success rate of 61% on opening novel cabinets and drawers in unseen environments zero-shot. An analysis of the failure modes suggests that errors in perception are the most significant challenge for our system. We will open source code and models for others to replicate and build upon our system.
|
[
"['Arjun Gupta' 'Michelle Zhang' 'Rishik Sathua' 'Saurabh Gupta']"
] |
null | null |
2402.17768
| null | null |
http://arxiv.org/pdf/2402.17768v2
|
2024-06-05T17:33:56Z
|
2024-02-27T18:59:18Z
|
Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning
|
A common failure mode for policies trained with imitation is compounding execution errors at test time. When the learned policy encounters states that are not present in the expert demonstrations, the policy fails, leading to degenerate behavior. The Dataset Aggregation, or DAgger approach to this problem simply collects more data to cover these failure states. However, in practice, this is often prohibitively expensive. In this work, we propose Diffusion Meets DAgger (DMD), a method to reap the benefits of DAgger without the cost for eye-in-hand imitation learning problems. Instead of collecting new samples to cover out-of-distribution states, DMD uses recent advances in diffusion models to synthesize these samples. This leads to robust performance from few demonstrations. We compare DMD against behavior cloning baseline across four tasks: pushing, stacking, pouring, and shirt hanging. In pushing, DMD achieves 80% success rate with as few as 8 expert demonstrations, where naive behavior cloning reaches only 20%. In stacking, DMD succeeds on average 92% of the time across 5 cups, versus 40% for BC. When pouring coffee beans, DMD transfers to another cup successfully 80% of the time. Finally, DMD attains 90% success rate for hanging shirt on a clothing rack.
|
[
"['Xiaoyu Zhang' 'Matthew Chang' 'Pranav Kumar' 'Saurabh Gupta']"
] |
null | null |
2402.17771
| null | null |
http://arxiv.org/pdf/2402.17771v1
|
2024-02-15T18:49:05Z
|
2024-02-15T18:49:05Z
|
Utilizing Machine Learning for Signal Classification and Noise Reduction
in Amateur Radio
|
In the realm of amateur radio, the effective classification of signals and the mitigation of noise play crucial roles in ensuring reliable communication. Traditional methods for signal classification and noise reduction often rely on manual intervention and predefined thresholds, which can be labor-intensive and less adaptable to dynamic radio environments. In this paper, we explore the application of machine learning techniques for signal classification and noise reduction in amateur radio operations. We investigate the feasibility and effectiveness of employing supervised and unsupervised learning algorithms to automatically differentiate between desired signals and unwanted interference, as well as to reduce the impact of noise on received transmissions. Experimental results demonstrate the potential of machine learning approaches to enhance the efficiency and robustness of amateur radio communication systems, paving the way for more intelligent and adaptive radio solutions in the amateur radio community.
|
[
"['Jimi Sanchez']"
] |
null | null |
2402.17772
| null | null |
http://arxiv.org/pdf/2402.17772v2
|
2024-06-18T06:31:49Z
|
2024-02-17T05:22:41Z
|
EEG2Rep: Enhancing Self-supervised EEG Representation Through
Informative Masked Inputs
|
Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representation learned, (2) The wide range of amplitudes from very small to relatively large due to factors such as the inter-subject variability, risks the models to be dominated by higher amplitude ranges, and (3) The absence of explicit segmentation in the continuous-valued sequences which can result in less informative representations. To address these challenges, we introduce textit{EEG2Rep}, a self-prediction approach for self-supervised representation learning from EEG. Two core novel components of EEG2Rep are as follows: 1) Instead of learning to predict the masked input from raw EEG, EEG2Rep learns to predict masked input in latent representation space, and 2) Instead of conventional masking methods, EEG2Rep uses a new semantic subsequence preserving (SSP) method which provides informative masked inputs to guide EEG2Rep to generate rich semantic representations. In experiments on 6 diverse EEG tasks with subject variability, EEG2Rep significantly outperforms state-of-the-art methods. We show that our semantic subsequence preserving improves the existing masking methods in self-prediction literature and find that preserving 50% of EEG recordings will result in the most accurate results on all 6 tasks on average. Finally, we show that EEG2Rep is robust to noise addressing a significant challenge that exists in EEG data. Models and code are available at:url{https://github.com/Navidfoumani/EEG2Rep}
|
[
"['Navid Mohammadi Foumani' 'Geoffrey Mackellar' 'Soheila Ghane'\n 'Saad Irtza' 'Nam Nguyen' 'Mahsa Salehi']"
] |
null | null |
2402.17773
| null | null |
http://arxiv.org/pdf/2402.17773v1
|
2024-02-17T20:03:02Z
|
2024-02-17T20:03:02Z
|
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel
Allocation in Cognitive Interference Networks
|
We consider the problem of dynamic channel allocation (DCA) in cognitive communication networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR) measure under a specified target quality of service (QoS)-SINR for each network. The shared bandwidth is partitioned into K channels with frequency separation. In contrast to the majority of existing studies that assume perfect orthogonality or a one- to-one user-channel allocation mapping, this paper focuses on real-world systems experiencing inter-carrier interference (ICI) and channel reuse by multiple large-scale networks. This realistic scenario significantly increases the problem dimension, rendering existing algorithms inefficient. We propose a novel multi-agent reinforcement learning (RL) framework for distributed DCA, named Channel Allocation RL To Overlapped Networks (CARLTON). The CARLTON framework is based on the Centralized Training with Decentralized Execution (CTDE) paradigm, utilizing the DeepMellow value-based RL algorithm. To ensure robust performance in the interference-laden environment we address, CARLTON employs a low-dimensional representation of observations, generating a QoS-type measure while maximizing a global SINR measure and ensuring the target QoS-SINR for each network. Our results demonstrate exceptional performance and robust generalization, showcasing superior efficiency compared to alternative state-of-the-art methods, while achieving a marginally diminished performance relative to a fully centralized approach.
|
[
"['Yaniv Cohen' 'Tomer Gafni' 'Ronen Greenberg' 'Kobi Cohen']"
] |
null | null |
2402.17775
| null | null |
http://arxiv.org/pdf/2402.17775v2
|
2024-06-26T14:34:13Z
|
2024-02-20T11:36:23Z
|
WhaleNet: a Novel Deep Learning Architecture for Marine Mammals
Vocalizations on Watkins Marine Mammal Sound Database
|
Marine mammal communication is a complex field, hindered by the diversity of vocalizations and environmental factors. The Watkins Marine Mammal Sound Database (WMMD) constitutes a comprehensive labeled dataset employed in machine learning applications. Nevertheless, the methodologies for data preparation, preprocessing, and classification documented in the literature exhibit considerable variability and are typically not applied to the dataset in its entirety. This study initially undertakes a concise review of the state-of-the-art benchmarks pertaining to the dataset, with a particular focus on clarifying data preparation and preprocessing techniques. Subsequently, we explore the utilization of the Wavelet Scattering Transform (WST) and Mel spectrogram as preprocessing mechanisms for feature extraction. In this paper, we introduce textbf{WhaleNet} (Wavelet Highly Adaptive Learning Ensemble Network), a sophisticated deep ensemble architecture for the classification of marine mammal vocalizations, leveraging both WST and Mel spectrogram for enhanced feature discrimination. By integrating the insights derived from WST and Mel representations, we achieved an improvement in classification accuracy by $8-10%$ over existing architectures, corresponding to a classification accuracy of $97.61%$.
|
[
"['Alessandro Licciardi' 'Davide Carbone']"
] |
null | null |
2402.17779
| null | null |
http://arxiv.org/pdf/2402.17779v1
|
2024-02-22T17:39:10Z
|
2024-02-22T17:39:10Z
|
Assessing the importance of long-range correlations for
deep-learning-based sleep staging
|
This study aims to elucidate the significance of long-range correlations for deep-learning-based sleep staging. It is centered around S4Sleep(TS), a recently proposed model for automated sleep staging. This model utilizes electroencephalography (EEG) as raw time series input and relies on structured state space sequence (S4) models as essential model component. Although the model already surpasses state-of-the-art methods for a moderate number of 15 input epochs, recent literature results suggest potential benefits from incorporating very long correlations spanning hundreds of input epochs. In this submission, we explore the possibility of achieving further enhancements by systematically scaling up the model's input size, anticipating potential improvements in prediction accuracy. In contrast to findings in literature, our results demonstrate that augmenting the input size does not yield a significant enhancement in the performance of S4Sleep(TS). These findings, coupled with the distinctive ability of S4 models to capture long-range dependencies in time series data, cast doubt on the diagnostic relevance of very long-range interactions for sleep staging.
|
[
"['Tiezhi Wang' 'Nils Strodthoff']"
] |
null | null |
2402.17780
| null | null |
http://arxiv.org/pdf/2402.17780v1
|
2024-02-23T02:31:35Z
|
2024-02-23T02:31:35Z
|
Constraint Latent Space Matters: An Anti-anomalous Waveform
Transformation Solution from Photoplethysmography to Arterial Blood Pressure
|
Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management. Notwithstanding its potential, the invasive nature of ABP measurements confines their utility primarily to clinical environments, limiting their applicability for continuous monitoring beyond medical facilities. The conversion of photoplethysmography (PPG) signals into ABP equivalents has garnered significant attention due to its potential in revolutionizing cardiovascular disease management. Recent strides in PPG-to-ABP prediction encompass the integration of generative and discriminative models. Despite these advances, the efficacy of these models is curtailed by the latent space shift predicament, stemming from alterations in PPG data distribution across disparate hardware and individuals, potentially leading to distorted ABP waveforms. To tackle this problem, we present an innovative solution named the Latent Space Constraint Transformer (LSCT), leveraging a quantized codebook to yield robust latent spaces by employing multiple discretizing bases. To facilitate improved reconstruction, the Correlation-boosted Attention Module (CAM) is introduced to systematically query pertinent bases on a global scale. Furthermore, to enhance expressive capacity, we propose the Multi-Spectrum Enhancement Knowledge (MSEK), which fosters local information flow within the channels of latent code and provides additional embedding for reconstruction. Through comprehensive experimentation on both publicly available datasets and a private downstream task dataset, the proposed approach demonstrates noteworthy performance enhancements compared to existing methods. Extensive ablation studies further substantiate the effectiveness of each introduced module.
|
[
"['Cheng Bian' 'Xiaoyu Li' 'Qi Bi' 'Guangpu Zhu' 'Jiegeng Lyu'\n 'Weile Zhang' 'Yelei Li' 'Zijing Zeng']"
] |
null | null |
2402.17783
| null | null |
http://arxiv.org/pdf/2402.17783v1
|
2024-02-24T04:13:48Z
|
2024-02-24T04:13:48Z
|
BagStacking: An Integrated Ensemble Learning Approach for Freezing of
Gait Detection in Parkinson's Disease
|
This paper introduces BagStacking, a novel ensemble learning method designed to enhance the detection of Freezing of Gait (FOG) in Parkinson's Disease (PD) by using a lower-back sensor to track acceleration. Building on the principles of bagging and stacking, BagStacking aims to achieve the variance reduction benefit of bagging's bootstrap sampling while also learning sophisticated blending through stacking. The method involves training a set of base models on bootstrap samples from the training data, followed by a meta-learner trained on the base model outputs and true labels to find an optimal aggregation scheme. The experimental evaluation demonstrates significant improvements over other state-of-the-art machine learning methods on the validation set. Specifically, BagStacking achieved a MAP score of 0.306, outperforming LightGBM (0.234) and classic Stacking (0.286). Additionally, the run-time of BagStacking was measured at 3828 seconds, illustrating an efficient approach compared to Regular Stacking's 8350 seconds. BagStacking presents a promising direction for handling the inherent variability in FOG detection data, offering a robust and scalable solution to improve patient care in PD.
|
[
"['Seffi Cohen' 'Lior Rokach']"
] |
null | null |
2402.17786
| null | null |
http://arxiv.org/pdf/2402.17786v1
|
2024-02-24T08:22:39Z
|
2024-02-24T08:22:39Z
|
Stepwise Self-Consistent Mathematical Reasoning with Large Language
Models
|
Using Large Language Models for complex mathematical reasoning is difficult, primarily due to the complexity of multi-step reasoning. The main challenges of this process include (1) selecting critical intermediate results to advance the procedure, and (2) limited exploration of potential solutions. To address these issues, we introduce a novel algorithm, namely Stepwise Self-Consistent Chain-of-Thought (SSC-CoT). SSC-CoT employs a strategy of selecting intermediate steps based on the intersection of various reasoning chains. Additionally, SSC-CoT enables the model to discover critical intermediate steps by querying a knowledge graph comprising relevant domain knowledge. To validate SSC-CoT, we present a new dataset, TriMaster100, tailored for complex trigonometry problems. This dataset contains 100 questions, with each solution broken down into scored intermediate steps, facilitating a comprehensive evaluation of the mathematical reasoning process. On TriMaster100, SSC-CoT triples the effectiveness of the state-of-the-art methods. Furthermore, we benchmark SSC-CoT on the widely recognized complex mathematical question dataset, MATH level 5, and it surpasses the second-best method by 7.2% in accuracy. Code and the TriMaster100 dataset can be found at: https://github.com/zhao-zilong/ssc-cot.
|
[
"['Zilong Zhao' 'Yao Rong' 'Dongyang Guo' 'Emek Gözlüklü' 'Emir Gülboy'\n 'Enkelejda Kasneci']"
] |
null | null |
2402.17788
| null | null |
http://arxiv.org/pdf/2402.17788v1
|
2024-02-24T16:29:36Z
|
2024-02-24T16:29:36Z
|
Multimodal Sleep Apnea Detection with Missing or Noisy Modalities
|
Polysomnography (PSG) is a type of sleep study that records multimodal physiological signals and is widely used for purposes such as sleep staging and respiratory event detection. Conventional machine learning methods assume that each sleep study is associated with a fixed set of observed modalities and that all modalities are available for each sample. However, noisy and missing modalities are a common issue in real-world clinical settings. In this study, we propose a comprehensive pipeline aiming to compensate for the missing or noisy modalities when performing sleep apnea detection. Unlike other existing studies, our proposed model works with any combination of available modalities. Our experiments show that the proposed model outperforms other state-of-the-art approaches in sleep apnea detection using various subsets of available data and different levels of noise, and maintains its high performance (AUROC>0.9) even in the presence of high levels of noise or missingness. This is especially relevant in settings where the level of noise and missingness is high (such as pediatric or outside-of-clinic scenarios).
|
[
"['Hamed Fayyaz' 'Abigail Strang' \"Niharika S. D'Souza\"\n 'Rahmatollah Beheshti']"
] |
null | null |
2402.17790
| null | null |
http://arxiv.org/pdf/2402.17790v1
|
2024-02-26T11:26:38Z
|
2024-02-26T11:26:38Z
|
EEG classifier cross-task transfer to avoid training sessions in
robot-assisted rehabilitation
|
Background: For an individualized support of patients during rehabilitation, learning of individual machine learning models from the human electroencephalogram (EEG) is required. Our approach allows labeled training data to be recorded without the need for a specific training session. For this, the planned exoskeleton-assisted rehabilitation enables bilateral mirror therapy, in which movement intentions can be inferred from the activity of the unaffected arm. During this therapy, labeled EEG data can be collected to enable movement predictions of only the affected arm of a patient. Methods: A study was conducted with 8 healthy subjects and the performance of the classifier transfer approach was evaluated. Each subject performed 3 runs of 40 self-intended unilateral and bilateral reaching movements toward a target while EEG data was recorded from 64 channels. A support vector machine (SVM) classifier was trained under both movement conditions to make predictions for the same type of movement. Furthermore, the classifier was evaluated to predict unilateral movements by only beeing trained on the data of the bilateral movement condition. Results: The results show that the performance of the classifier trained on selected EEG channels evoked by bilateral movement intentions is not significantly reduced compared to a classifier trained directly on EEG data including unilateral movement intentions. Moreover, the results show that our approach also works with only 8 or even 4 channels. Conclusion: It was shown that the proposed classifier transfer approach enables motion prediction without explicit collection of training data. Since the approach can be applied even with a small number of EEG channels, this speaks for the feasibility of the approach in real therapy sessions with patients and motivates further investigations with stroke patients.
|
[
"['Niklas Kueper' 'Su Kyoung Kim' 'Elsa Andrea Kirchner']"
] |
null | null |
2402.17791
| null | null |
http://arxiv.org/abs/2402.17791v1
|
2024-02-26T12:28:51Z
|
2024-02-26T12:28:51Z
|
Label Informed Contrastive Pretraining for Node Importance Estimation on
Knowledge Graphs
|
Node Importance Estimation (NIE) is a task of inferring importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicating to knowledge graphs for predicting future or missing node importance scores. Existing state-of-the-art NIE methods train the model by available labels, and they consider every interested node equally before training. However, the nodes with higher importance often require or receive more attention in real-world scenarios, e.g., people may care more about the movies or webpages with higher importance. To this end, we introduce Label Informed ContrAstive Pretraining (LICAP) to the NIE problem for being better aware of the nodes with high importance scores. Specifically, LICAP is a novel type of contrastive learning framework that aims to fully utilize the continuous labels to generate contrastive samples for pretraining embeddings. Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores. The contrastive samples are generated from those bins, and are then used to pretrain node embeddings of knowledge graphs via a newly proposed Predicate-aware Graph Attention Networks (PreGAT), so as to better separate the top nodes from non-top nodes, and distinguish the top nodes within top bin by keeping the relative order among finer bins. Extensive experiments demonstrate that the LICAP pretrained embeddings can further boost the performance of existing NIE methods and achieve the new state-of-the-art performance regarding both regression and ranking metrics. The source code for reproducibility is available at https://github.com/zhangtia16/LICAP
|
[
"['Tianyu Zhang' 'Chengbin Hou' 'Rui Jiang' 'Xuegong Zhang' 'Chenghu Zhou'\n 'Ke Tang' 'Hairong Lv']"
] |
null | null |
2402.17792
| null | null |
http://arxiv.org/pdf/2402.17792v1
|
2024-02-26T15:11:41Z
|
2024-02-26T15:11:41Z
|
EGNN-C+: Interpretable Evolving Granular Neural Network and Application
in Classification of Weakly-Supervised EEG Data Streams
|
We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals. Emotion recognition is crucial for enhancing the realism and interactivity of computer systems. We extract features from the Fourier spectrum of EEG signals obtained from 28 individuals engaged in playing computer games -- a public dataset. Each game elicits a different predominant emotion: boredom, calmness, horror, or joy. We analyze individual electrodes, time window lengths, and frequency bands to assess the accuracy and interpretability of resulting user-independent neural models. The findings indicate that both brain hemispheres assist classification, especially electrodes on the temporal (T8) and parietal (P7) areas, alongside contributions from frontal and occipital electrodes. While patterns may manifest in any band, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, exhibited higher correspondence with the emotion classes. The eGNN-C+ demonstrates effectiveness in learning EEG data. It achieves an accuracy of 81.7% and a 0.0029 II interpretability using 10-second time windows, even in face of a highly-stochastic time-varying 4-class classification problem.
|
[
"['Daniel Leite' 'Alisson Silva' 'Gabriella Casalino' 'Arnab Sharma'\n 'Danielle Fortunato' 'Axel-Cyrille Ngomo']"
] |
null | null |
2402.17793
| null | null |
http://arxiv.org/pdf/2402.17793v1
|
2024-02-26T18:37:18Z
|
2024-02-26T18:37:18Z
|
A Surprising Failure? Multimodal LLMs and the NLVR Challenge
|
This study evaluates three state-of-the-art MLLMs -- GPT-4V, Gemini Pro, and the open-source model IDEFICS -- on the compositional natural language vision reasoning task NLVR. Given a human-written sentence paired with a synthetic image, this task requires the model to determine the truth value of the sentence with respect to the image. Despite the strong performance demonstrated by these models, we observe they perform poorly on NLVR, which was constructed to require compositional and spatial reasoning, and to be robust for semantic and systematic biases.
|
[
"['Anne Wu' 'Kianté Brantley' 'Yoav Artzi']"
] |
null | null |
2402.17802
| null | null |
http://arxiv.org/pdf/2402.17802v2
|
2024-04-02T21:26:01Z
|
2024-02-27T08:34:48Z
|
Time Series Analysis in Compressor-Based Machines: A Survey
|
In both industrial and residential contexts, compressor-based machines, such as refrigerators, HVAC systems, heat pumps and chillers, are essential to fulfil production and consumers' needs. The diffusion of sensors and IoT connectivity supports the development of monitoring systems that can detect and predict faults, identify behavioural shifts and forecast the operational status of machines and their components. The focus of this paper is to survey the recent research on such tasks as FD, FP, Forecasting and CPD applied to multivariate time series characterizing the operations of compressor-based machines. These tasks play a critical role in improving the efficiency and longevity of machines by minimizing downtime and maintenance costs and improving the energy efficiency. Specifically, FD detects and diagnoses faults, FP predicts such occurrences, forecasting anticipates the future value of characteristic variables of machines and CPD identifies significant variations in the behaviour of the appliances, such as a change in the working regime. We identify and classify the approaches to the tasks mentioned above, compare the algorithms employed, highlight the gaps in the current status of the art and discuss the most promising future research directions in the field.
|
[
"['Francesca Forbicini' 'Nicolò Oreste Pinciroli Vago' 'Piero Fraternali']"
] |
null | null |
2402.17804
| null | null |
http://arxiv.org/abs/2402.17804v1
|
2024-02-27T09:07:59Z
|
2024-02-27T09:07:59Z
|
Predicting machine failures from multivariate time series: an industrial
case study
|
Non-neural Machine Learning (ML) and Deep Learning (DL) models are often used to predict system failures in the context of industrial maintenance. However, only a few researches jointly assess the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three data sets concerning the operation of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. The problem is formulated as a binary classification task that assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared using multivariate telemetry time series. The results indicate that, in the considered scenarios, the dimension of the prediction windows plays a crucial role and highlight the effectiveness of DL approaches at classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches at classifying similar and repetitive patterns preceding a failure.
|
[
"['Nicolò Oreste Pinciroli Vago' 'Francesca Forbicini' 'Piero Fraternali']"
] |
null | null |
2402.17805
| null | null |
http://arxiv.org/pdf/2402.17805v1
|
2024-02-27T11:04:06Z
|
2024-02-27T11:04:06Z
|
Graph Neural Networks and Arithmetic Circuits
|
We characterize the computational power of neural networks that follow the graph neural network (GNN) architecture, not restricted to aggregate-combine GNNs or other particular types. We establish an exact correspondence between the expressivity of GNNs using diverse activation functions and arithmetic circuits over real numbers. In our results the activation function of the network becomes a gate type in the circuit. Our result holds for families of constant depth circuits and networks, both uniformly and non-uniformly, for all common activation functions.
|
[
"['Timon Barlag' 'Vivian Holzapfel' 'Laura Strieker' 'Jonni Virtema'\n 'Heribert Vollmer']"
] |
null | null |
2402.17806
| null | null |
http://arxiv.org/pdf/2402.17806v1
|
2024-02-27T11:27:32Z
|
2024-02-27T11:27:32Z
|
Material Microstructure Design Using VAE-Regression with Multimodal
Prior
|
We propose a variational autoencoder (VAE)-based model for building forward and inverse structure-property linkages, a problem of paramount importance in computational materials science. Our model systematically combines VAE with regression, linking the two models through a two-level prior conditioned on the regression variables. The regression loss is optimized jointly with the reconstruction loss of the variational autoencoder, learning microstructure features relevant for property prediction and reconstruction. The resultant model can be used for both forward and inverse prediction i.e., for predicting the properties of a given microstructure as well as for predicting the microstructure required to obtain given properties. Since the inverse problem is ill-posed (one-to-many), we derive the objective function using a multi-modal Gaussian mixture prior enabling the model to infer multiple microstructures for a target set of properties. We show that for forward prediction, our model is as accurate as state-of-the-art forward-only models. Additionally, our method enables direct inverse inference. We show that the microstructures inferred using our model achieve desired properties reasonably accurately, avoiding the need for expensive optimization loops.
|
[
"['Avadhut Sardeshmukh' 'Sreedhar Reddy' 'BP Gautham'\n 'Pushpak Bhattacharyya']"
] |
null | null |
2402.17807
| null | null |
http://arxiv.org/pdf/2402.17807v1
|
2024-02-27T11:29:36Z
|
2024-02-27T11:29:36Z
|
Exploring Gene Regulatory Interaction Networks and predicting
therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung
adenocarcinoma
|
With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously.
|
[
"['Abanti Bhattacharjya' 'Md Manowarul Islam' 'Md Ashraf Uddin'\n 'Md. Alamin Talukder' 'AKM Azad' 'Sunil Aryal' 'Bikash Kumar Paul'\n 'Wahia Tasnim' 'Muhammad Ali Abdulllah Almoyad' 'Mohammad Ali Moni']"
] |
null | null |
2402.17808
| null | null |
http://arxiv.org/pdf/2402.17808v1
|
2024-02-27T11:42:26Z
|
2024-02-27T11:42:26Z
|
AN An ica-ensemble learning approach for prediction of uwb nlos signals
data classification
|
Trapped human detection in search and rescue (SAR) scenarios poses a significant challenge in pervasive computing. This study addresses this issue by leveraging machine learning techniques, given their high accuracy. However, accurate identification of trapped individuals is hindered by the curse of dimensionality and noisy data. Particularly in non-line-of-sight (NLOS) situations during catastrophic events, the curse of dimensionality may lead to blind spots due to noise and uncorrelated values in detections. This research focuses on harmonizing information through wireless communication and identifying individuals in NLOS scenarios using ultra-wideband (UWB) radar signals. Employing independent component analysis (ICA) for feature extraction, the study evaluates classification performance using ensemble algorithms on both static and dynamic datasets. The experimental results demonstrate categorization accuracies of 88.37% for static data and 87.20% for dynamic data, highlighting the effectiveness of the proposed approach. Finally, this work can help scientists and engineers make instant decisions during SAR operations.
|
[
"['Jiya A. Enoch' 'Ilesanmi B. Oluwafemi' 'Francis A. Ibikunle'\n 'Olulope K. Paul']"
] |
null | null |
2402.17810
| null | null |
http://arxiv.org/pdf/2402.17810v2
|
2024-05-31T14:07:00Z
|
2024-02-27T12:43:09Z
|
BioT5+: Towards Generalized Biological Understanding with IUPAC
Integration and Multi-task Tuning
|
Recent research trends in computational biology have increasingly focused on integrating text and bio-entity modeling, especially in the context of molecules and proteins. However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual representations (e.g., IUPAC). This paper introduces BioT5+, an extension of the BioT5 framework, tailored to enhance biological research and drug discovery. BioT5+ incorporates several novel features: integration of IUPAC names for molecular understanding, inclusion of extensive bio-text and molecule data from sources like bioRxiv and PubChem, the multi-task instruction tuning for generality across tasks, and a numerical tokenization technique for improved processing of numerical data. These enhancements allow BioT5+ to bridge the gap between molecular representations and their textual descriptions, providing a more holistic understanding of biological entities, and largely improving the grounded reasoning of bio-text and bio-sequences. The model is pre-trained and fine-tuned with a large number of experiments, including emph{3 types of problems (classification, regression, generation), 15 kinds of tasks, and 21 total benchmark datasets}, demonstrating the remarkable performance and state-of-the-art results in most cases. BioT5+ stands out for its ability to capture intricate relationships in biological data, thereby contributing significantly to bioinformatics and computational biology. Our code is available at url{https://github.com/QizhiPei/BioT5}.
|
[
"['Qizhi Pei' 'Lijun Wu' 'Kaiyuan Gao' 'Xiaozhuan Liang' 'Yin Fang'\n 'Jinhua Zhu' 'Shufang Xie' 'Tao Qin' 'Rui Yan']"
] |
null | null |
2402.17811
| null | null |
http://arxiv.org/pdf/2402.17811v2
|
2024-06-05T11:15:04Z
|
2024-02-27T14:45:04Z
|
TruthX: Alleviating Hallucinations by Editing Large Language Models in
Truthful Space
|
Large Language Models (LLMs) sometimes suffer from producing hallucinations, especially LLMs may generate untruthful responses despite knowing the correct knowledge. Activating the truthfulness within LLM is the key to fully unlocking LLM's knowledge potential. In this paper, we propose TruthX, an inference-time intervention method to activate the truthfulness of LLM by identifying and editing the features within LLM's internal representations that govern the truthfulness. TruthX employs an auto-encoder to map LLM's representations into semantic and truthful latent spaces respectively, and applies contrastive learning to identify a truthful editing direction within the truthful space. During inference, by editing LLM's internal representations in truthful space, TruthX effectively enhances the truthfulness of LLM. Experiments show that TruthX improves the truthfulness of 13 advanced LLMs by an average of 20% on TruthfulQA benchmark. Further analyses suggest that TruthX can control LLM to produce truthful or hallucinatory responses via editing only one vector in LLM's internal representations.
|
[
"['Shaolei Zhang' 'Tian Yu' 'Yang Feng']"
] |
null | null |
2402.17812
| null | null |
http://arxiv.org/pdf/2402.17812v1
|
2024-02-27T14:51:11Z
|
2024-02-27T14:51:11Z
|
DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping
Backward Propagation
|
Training deep neural networks typically involves substantial computational costs during both forward and backward propagation. The conventional layer dropping techniques drop certain layers during training for reducing the computations burden. However, dropping layers during forward propagation adversely affects the training process by degrading accuracy. In this paper, we propose Dropping Backward Propagation (DropBP), a novel approach designed to reduce computational costs while maintaining accuracy. DropBP randomly drops layers during the backward propagation, which does not deviate forward propagation. Moreover, DropBP calculates the sensitivity of each layer to assign appropriate drop rate, thereby stabilizing the training process. DropBP is designed to enhance the efficiency of the training process with backpropagation, thereby enabling the acceleration of both full fine-tuning and parameter-efficient fine-tuning using backpropagation. Specifically, utilizing DropBP in QLoRA reduces training time by 44%, increases the convergence speed to the identical loss level by 1.5$times$, and enables training with a 6.2$times$ larger sequence length on a single NVIDIA-A100 80GiB GPU in LLaMA2-70B. The code is available at https://github.com/WooSunghyeon/dropbp.
|
[
"['Sunghyeon Woo' 'Baeseong Park' 'Byeongwook Kim' 'Minjung Jo'\n 'Sejung Kwon' 'Dongsuk Jeon' 'Dongsoo Lee']"
] |
null | null |
2402.17826
| null | null |
http://arxiv.org/pdf/2402.17826v2
|
2024-05-23T16:50:21Z
|
2024-02-27T19:00:01Z
|
Prediction-Powered Ranking of Large Language Models
|
Large language models are often ranked according to their level of alignment with human preferences -- a model is better than other models if its outputs are more frequently preferred by humans. One of the popular ways to elicit human preferences utilizes pairwise comparisons between the outputs provided by different models to the same inputs. However, since gathering pairwise comparisons by humans is costly and time-consuming, it has become a common practice to gather pairwise comparisons by a strong large language model -- a model strongly aligned with human preferences. Surprisingly, practitioners cannot currently measure the uncertainty that any mismatch between human and model preferences may introduce in the constructed rankings. In this work, we develop a statistical framework to bridge this gap. Given a (small) set of pairwise comparisons by humans and a large set of pairwise comparisons by a model, our framework provides a rank-set -- a set of possible ranking positions -- for each of the models under comparison. Moreover, it guarantees that, with a probability greater than or equal to a user-specified value, the rank-sets cover the true ranking consistent with the distribution of human pairwise preferences asymptotically. Using pairwise comparisons made by humans in the LMSYS Chatbot Arena platform and pairwise comparisons made by three strong large language models, we empirically demonstrate the effectivity of our framework and show that the rank-sets constructed using only pairwise comparisons by the strong large language models are often inconsistent with (the distribution of) human pairwise preferences.
|
[
"['Ivi Chatzi' 'Eleni Straitouri' 'Suhas Thejaswi' 'Manuel Gomez Rodriguez']"
] |
null | null |
2402.17840
| null | null |
http://arxiv.org/pdf/2402.17840v2
|
2024-06-20T21:32:35Z
|
2024-02-27T19:08:05Z
|
Follow My Instruction and Spill the Beans: Scalable Data Extraction from
Retrieval-Augmented Generation Systems
|
Retrieval-Augmented Generation (RAG) improves pre-trained models by incorporating external knowledge at test time to enable customized adaptation. We study the risk of datastore leakage in Retrieval-In-Context RAG Language Models (LMs). We show that an adversary can exploit LMs' instruction-following capabilities to easily extract text data verbatim from the datastore of RAG systems built with instruction-tuned LMs via prompt injection. The vulnerability exists for a wide range of modern LMs that span Llama2, Mistral/Mixtral, Vicuna, SOLAR, WizardLM, Qwen1.5, and Platypus2, and the exploitability exacerbates as the model size scales up. Extending our study to production RAG models GPTs, we design an attack that can cause datastore leakage with a 100% success rate on 25 randomly selected customized GPTs with at most 2 queries, and we extract text data verbatim at a rate of 41% from a book of 77,000 words and 3% from a corpus of 1,569,000 words by prompting the GPTs with only 100 queries generated by themselves.
|
[
"['Zhenting Qi' 'Hanlin Zhang' 'Eric Xing' 'Sham Kakade'\n 'Himabindu Lakkaraju']"
] |
null | null |
2402.17853
| null | null |
http://arxiv.org/pdf/2402.17853v1
|
2024-02-27T19:36:27Z
|
2024-02-27T19:36:27Z
|
Latent Neural PDE Solver: a reduced-order modelling framework for
partial differential equations
|
Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs). Different from many existing neural network surrogates operating on high-dimensional discretized fields, we propose to learn the dynamics of the system in the latent space with much coarser discretizations. In our proposed framework - Latent Neural PDE Solver (LNS), a non-linear autoencoder is first trained to project the full-order representation of the system onto the mesh-reduced space, then a temporal model is trained to predict the future state in this mesh-reduced space. This reduction process simplifies the training of the temporal model by greatly reducing the computational cost accompanying a fine discretization. We study the capability of the proposed framework and several other popular neural PDE solvers on various types of systems including single-phase and multi-phase flows along with varying system parameters. We showcase that it has competitive accuracy and efficiency compared to the neural PDE solver that operates on full-order space.
|
[
"['Zijie Li' 'Saurabh Patil' 'Francis Ogoke' 'Dule Shu' 'Wilson Zhen'\n 'Michael Schneier' 'John R. Buchanan, Jr.' 'Amir Barati Farimani']"
] |
null | null |
2402.17870
| null | null |
http://arxiv.org/pdf/2402.17870v1
|
2024-02-27T20:10:03Z
|
2024-02-27T20:10:03Z
|
Stochastic Approximation with Biased MCMC for Expectation Maximization
|
The expectation maximization (EM) algorithm is a widespread method for empirical Bayesian inference, but its expectation step (E-step) is often intractable. Employing a stochastic approximation scheme with Markov chain Monte Carlo (MCMC) can circumvent this issue, resulting in an algorithm known as MCMC-SAEM. While theoretical guarantees for MCMC-SAEM have previously been established, these results are restricted to the case where asymptotically unbiased MCMC algorithms are used. In practice, MCMC-SAEM is often run with asymptotically biased MCMC, for which the consequences are theoretically less understood. In this work, we fill this gap by analyzing the asymptotics and non-asymptotics of SAEM with biased MCMC steps, particularly the effect of bias. We also provide numerical experiments comparing the Metropolis-adjusted Langevin algorithm (MALA), which is asymptotically unbiased, and the unadjusted Langevin algorithm (ULA), which is asymptotically biased, on synthetic and real datasets. Experimental results show that ULA is more stable with respect to the choice of Langevin stepsize and can sometimes result in faster convergence.
|
[
"['Samuel Gruffaz' 'Kyurae Kim' 'Alain Oliviero Durmus' 'Jacob R. Gardner']"
] |
null | null |
2402.17879
| null | null |
http://arxiv.org/pdf/2402.17879v2
|
2024-06-22T05:08:30Z
|
2024-02-27T20:33:22Z
|
Automated Statistical Model Discovery with Language Models
|
Statistical model discovery is a challenging search over a vast space of models subject to domain-specific constraints. Efficiently searching over this space requires expertise in modeling and the problem domain. Motivated by the domain knowledge and programming capabilities of large language models (LMs), we introduce a method for language model driven automated statistical model discovery. We cast our automated procedure within the principled framework of Box's Loop: the LM iterates between proposing statistical models represented as probabilistic programs, acting as a modeler, and critiquing those models, acting as a domain expert. By leveraging LMs, we do not have to define a domain-specific language of models or design a handcrafted search procedure, which are key restrictions of previous systems. We evaluate our method in three settings in probabilistic modeling: searching within a restricted space of models, searching over an open-ended space, and improving expert models under natural language constraints (e.g., this model should be interpretable to an ecologist). Our method identifies models on par with human expert designed models and extends classic models in interpretable ways. Our results highlight the promise of LM-driven model discovery.
|
[
"['Michael Y. Li' 'Emily B. Fox' 'Noah D. Goodman']"
] |
null | null |
2402.17885
| null | null |
http://arxiv.org/pdf/2402.17885v1
|
2024-02-27T20:57:35Z
|
2024-02-27T20:57:35Z
|
Independent Learning in Constrained Markov Potential Games
|
Constrained Markov games offer a formal mathematical framework for modeling multi-agent reinforcement learning problems where the behavior of the agents is subject to constraints. In this work, we focus on the recently introduced class of constrained Markov Potential Games. While centralized algorithms have been proposed for solving such constrained games, the design of converging independent learning algorithms tailored for the constrained setting remains an open question. We propose an independent policy gradient algorithm for learning approximate constrained Nash equilibria: Each agent observes their own actions and rewards, along with a shared state. Inspired by the optimization literature, our algorithm performs proximal-point-like updates augmented with a regularized constraint set. Each proximal step is solved inexactly using a stochastic switching gradient algorithm. Notably, our algorithm can be implemented independently without a centralized coordination mechanism requiring turn-based agent updates. Under some technical constraint qualification conditions, we establish convergence guarantees towards constrained approximate Nash equilibria. We perform simulations to illustrate our results.
|
[
"['Philip Jordan' 'Anas Barakat' 'Niao He']"
] |
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