categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.04429
| null | null |
http://arxiv.org/pdf/2403.04429v1
|
2024-03-07T11:59:00Z
|
2024-03-07T11:59:00Z
|
Exploring the Influence of Dimensionality Reduction on Anomaly Detection
Performance in Multivariate Time Series
|
This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing for a robust assessment of the models' capabilities in varied contexts. The dimensionality reduction techniques examined include PCA, UMAP, Random Projection, and t-SNE, each offering distinct advantages in simplifying high-dimensional data. Our findings reveal that dimensionality reduction not only aids in reducing computational complexity but also significantly enhances anomaly detection performance in certain scenarios. Moreover, a remarkable reduction in training times was observed, with reductions by approximately 300% and 650% when dimensionality was halved and minimized to the lowest dimensions, respectively. This efficiency gain underscores the dual benefit of dimensionality reduction in both performance enhancement and operational efficiency. The MUTANT model exhibits notable adaptability, especially with UMAP reduction, while the Anomaly-Transformer demonstrates versatility across various reduction techniques. These insights provide a deeper understanding of the synergistic effects of dimensionality reduction and anomaly detection, contributing valuable perspectives to the field of time series analysis. The study underscores the importance of selecting appropriate dimensionality reduction strategies based on specific model requirements and dataset characteristics, paving the way for more efficient, accurate, and scalable solutions in anomaly detection.
|
[
"['Mahsun Altin' 'Altan Cakir']"
] |
null | null |
2403.04430
| null | null |
http://arxiv.org/pdf/2403.04430v1
|
2024-03-07T12:00:33Z
|
2024-03-07T12:00:33Z
|
On-demand Quantization for Green Federated Generative Diffusion in
Mobile Edge Networks
|
Generative Artificial Intelligence (GAI) shows remarkable productivity and creativity in Mobile Edge Networks, such as the metaverse and the Industrial Internet of Things. Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution. However, there is a notable issue with communication consumption when training large GAI models like generative diffusion models in mobile edge networks. Additionally, the substantial energy consumption associated with training diffusion-based models, along with the limited resources of edge devices and complexities of network environments, pose challenges for improving the training efficiency of GAI models. To address this challenge, we propose an on-demand quantized energy-efficient federated diffusion approach for mobile edge networks. Specifically, we first design a dynamic quantized federated diffusion training scheme considering various demands from the edge devices. Then, we study an energy efficiency problem based on specific quantization requirements. Numerical results show that our proposed method significantly reduces system energy consumption and transmitted model size compared to both baseline federated diffusion and fixed quantized federated diffusion methods while effectively maintaining reasonable quality and diversity of generated data.
|
[
"['Bingkun Lai' 'Jiayi He' 'Jiawen Kang' 'Gaolei Li' 'Minrui Xu'\n 'Tao zhang' 'Shengli Xie']"
] |
null | null |
2403.04431
| null | null |
http://arxiv.org/pdf/2403.04431v1
|
2024-03-07T12:03:04Z
|
2024-03-07T12:03:04Z
|
Boosting Fairness and Robustness in Over-the-Air Federated Learning
|
Over-the-Air Computation is a beyond-5G communication strategy that has recently been shown to be useful for the decentralized training of machine learning models due to its efficiency. In this paper, we propose an Over-the-Air federated learning algorithm that aims to provide fairness and robustness through minmax optimization. By using the epigraph form of the problem at hand, we show that the proposed algorithm converges to the optimal solution of the minmax problem. Moreover, the proposed approach does not require reconstructing channel coefficients by complex encoding-decoding schemes as opposed to state-of-the-art approaches. This improves both efficiency and privacy.
|
[
"['Halil Yigit Oksuz' 'Fabio Molinari' 'Henning Sprekeler' 'Joerg Raisch']"
] |
null | null |
2403.04436
| null | null |
http://arxiv.org/pdf/2403.04436v1
|
2024-03-07T12:10:41Z
|
2024-03-07T12:10:41Z
|
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
|
We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB camera. To create a large-scale retargeted motion dataset of human movements for humanoid robots, we propose a scalable "sim-to-data" process to filter and pick feasible motions using a privileged motion imitator. Afterwards, we train a robust real-time humanoid motion imitator in simulation using these refined motions and transfer it to the real humanoid robot in a zero-shot manner. We successfully achieve teleoperation of dynamic whole-body motions in real-world scenarios, including walking, back jumping, kicking, turning, waving, pushing, boxing, etc. To the best of our knowledge, this is the first demonstration to achieve learning-based real-time whole-body humanoid teleoperation.
|
[
"['Tairan He' 'Zhengyi Luo' 'Wenli Xiao' 'Chong Zhang' 'Kris Kitani'\n 'Changliu Liu' 'Guanya Shi']"
] |
null | null |
2403.04442
| null | null |
http://arxiv.org/abs/2403.04442v1
|
2024-03-07T12:16:51Z
|
2024-03-07T12:16:51Z
|
Cooperative Bayesian Optimization for Imperfect Agents
|
We introduce a cooperative Bayesian optimization problem for optimizing black-box functions of two variables where two agents choose together at which points to query the function but have only control over one variable each. This setting is inspired by human-AI teamwork, where an AI-assistant helps its human user solve a problem, in this simplest case, collaborative optimization. We formulate the solution as sequential decision-making, where the agent we control models the user as a computationally rational agent with prior knowledge about the function. We show that strategic planning of the queries enables better identification of the global maximum of the function as long as the user avoids excessive exploration. This planning is made possible by using Bayes Adaptive Monte Carlo planning and by endowing the agent with a user model that accounts for conservative belief updates and exploratory sampling of the points to query.
|
[
"['Ali Khoshvishkaie' 'Petrus Mikkola' 'Pierre-Alexandre Murena'\n 'Samuel Kaski']"
] |
null | null |
2403.04447
| null | null |
http://arxiv.org/pdf/2403.04447v1
|
2024-03-07T12:34:03Z
|
2024-03-07T12:34:03Z
|
FRRI: a novel algorithm for fuzzy-rough rule induction
|
Interpretability is the next frontier in machine learning research. In the search for white box models - as opposed to black box models, like random forests or neural networks - rule induction algorithms are a logical and promising option, since the rules can easily be understood by humans. Fuzzy and rough set theory have been successfully applied to this archetype, almost always separately. As both approaches to rule induction involve granular computing based on the concept of equivalence classes, it is natural to combine them. The QuickRulescite{JensenCornelis2009} algorithm was a first attempt at using fuzzy rough set theory for rule induction. It is based on QuickReduct, a greedy algorithm for building decision reducts. QuickRules already showed an improvement over other rule induction methods. However, to evaluate the full potential of a fuzzy rough rule induction algorithm, one needs to start from the foundations. In this paper, we introduce a novel rule induction algorithm called Fuzzy Rough Rule Induction (FRRI). We provide background and explain the workings of our algorithm. Furthermore, we perform a computational experiment to evaluate the performance of our algorithm and compare it to other state-of-the-art rule induction approaches. We find that our algorithm is more accurate while creating small rulesets consisting of relatively short rules. We end the paper by outlining some directions for future work.
|
[
"['Henri Bollaert' 'Marko Palangetić' 'Chris Cornelis' 'Salvatore Greco'\n 'Roman Słowiński']"
] |
null | null |
2403.04451
| null | null |
http://arxiv.org/pdf/2403.04451v1
|
2024-03-07T12:43:42Z
|
2024-03-07T12:43:42Z
|
Membership Inference Attacks and Privacy in Topic Modeling
|
Recent research shows that large language models are susceptible to privacy attacks that infer aspects of the training data. However, it is unclear if simpler generative models, like topic models, share similar vulnerabilities. In this work, we propose an attack against topic models that can confidently identify members of the training data in Latent Dirichlet Allocation. Our results suggest that the privacy risks associated with generative modeling are not restricted to large neural models. Additionally, to mitigate these vulnerabilities, we explore differentially private (DP) topic modeling. We propose a framework for private topic modeling that incorporates DP vocabulary selection as a pre-processing step, and show that it improves privacy while having limited effects on practical utility.
|
[
"['Nico Manzonelli' 'Wanrong Zhang' 'Salil Vadhan']"
] |
null | null |
2403.04453
| null | null |
http://arxiv.org/pdf/2403.04453v2
|
2024-06-20T11:06:30Z
|
2024-03-07T12:45:51Z
|
Vlearn: Off-Policy Learning with Efficient State-Value Function
Estimation
|
Existing off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation, which can be problematic in high-dimensional action spaces due to the curse of dimensionality. This reliance results in data inefficiency as maintaining a state-action-value function in such spaces is challenging. We present an efficient approach that utilizes only a state-value function as the critic for off-policy deep reinforcement learning. This approach, which we refer to as Vlearn, effectively circumvents the limitations of existing methods by eliminating the necessity for an explicit state-action-value function. To this end, we introduce a novel importance sampling loss for learning deep value functions from off-policy data. While this is common for linear methods, it has not been combined with deep value function networks. This transfer to deep methods is not straightforward and requires novel design choices such as robust policy updates, twin value function networks to avoid an optimization bias, and importance weight clipping. We also present a novel analysis of the variance of our estimate compared to commonly used importance sampling estimators such as V-trace. Our approach improves sample complexity as well as final performance and ensures consistent and robust performance across various benchmark tasks. Eliminating the state-action-value function in Vlearn facilitates a streamlined learning process, enabling more effective exploration and exploitation in complex environments.
|
[
"['Fabian Otto' 'Philipp Becker' 'Ngo Anh Vien' 'Gerhard Neumann']"
] |
null | null |
2403.04468
| null | null |
http://arxiv.org/pdf/2403.04468v1
|
2024-03-07T13:10:37Z
|
2024-03-07T13:10:37Z
|
A Survey of Graph Neural Networks in Real world: Imbalance, Noise,
Privacy and OOD Challenges
|
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Neural Networks (GNNs) to achieve remarkable success in these areas. However, in real-world scenarios, the training environment for models is often far from ideal, leading to substantial performance degradation of GNN models due to various unfavorable factors, including imbalance in data distribution, the presence of noise in erroneous data, privacy protection of sensitive information, and generalization capability for out-of-distribution (OOD) scenarios. To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness. In this paper, we present a comprehensive survey that systematically reviews existing GNN models, focusing on solutions to the four mentioned real-world challenges including imbalance, noise, privacy, and OOD in practical scenarios that many existing reviews have not considered. Specifically, we first highlight the four key challenges faced by existing GNNs, paving the way for our exploration of real-world GNN models. Subsequently, we provide detailed discussions on these four aspects, dissecting how these solutions contribute to enhancing the reliability and robustness of GNN models. Last but not least, we outline promising directions and offer future perspectives in the field.
|
[
"['Wei Ju' 'Siyu Yi' 'Yifan Wang' 'Zhiping Xiao' 'Zhengyang Mao'\n 'Hourun Li' 'Yiyang Gu' 'Yifang Qin' 'Nan Yin' 'Senzhang Wang'\n 'Xinwang Liu' 'Xiao Luo' 'Philip S. Yu' 'Ming Zhang']"
] |
null | null |
2403.04477
| null | null |
http://arxiv.org/pdf/2403.04477v1
|
2024-03-07T13:22:25Z
|
2024-03-07T13:22:25Z
|
Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
|
Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep learning in time series forecasting remain ambiguous. Our research primarily focuses on examining the impact of specific hyperparameters related to time series, such as context length and validation strategy, on the performance of the state-of-the-art MLP model in time series forecasting. We have conducted a comprehensive series of experiments involving 4800 configurations per dataset across 20 time series forecasting datasets, and our findings demonstrate the importance of tuning these parameters. Furthermore, in this work, we introduce the largest metadataset for timeseries forecasting to date, named TSBench, comprising 97200 evaluations, which is a twentyfold increase compared to previous works in the field. Finally, we demonstrate the utility of the created metadataset on multi-fidelity hyperparameter optimization tasks.
|
[
"['Kiran Madhusudhanan' 'Shayan Jawed' 'Lars Schmidt-Thieme']"
] |
null | null |
2403.04482
| null | null |
http://arxiv.org/pdf/2403.04482v2
|
2024-07-08T14:49:14Z
|
2024-03-07T13:33:30Z
|
On the Topology Awareness and Generalization Performance of Graph Neural
Networks
|
Many computer vision and machine learning problems are modelled as learning tasks on graphs where graph neural networks GNNs have emerged as a dominant tool for learning representations of graph structured data A key feature of GNNs is their use of graph structures as input enabling them to exploit the graphs inherent topological properties known as the topology awareness of GNNs Despite the empirical successes of GNNs the influence of topology awareness on generalization performance remains unexplored, particularly for node level tasks that diverge from the assumption of data being independent and identically distributed IID The precise definition and characterization of the topology awareness of GNNs especially concerning different topological features are still unclear This paper introduces a comprehensive framework to characterize the topology awareness of GNNs across any topological feature Using this framework we investigate the effects of topology awareness on GNN generalization performance Contrary to the prevailing belief that enhancing the topology awareness of GNNs is always advantageous our analysis reveals a critical insight improving the topology awareness of GNNs may inadvertently lead to unfair generalization across structural groups which might not be desired in some scenarios Additionally we conduct a case study using the intrinsic graph metric the shortest path distance on various benchmark datasets The empirical results of this case study confirm our theoretical insights Moreover we demonstrate the practical applicability of our framework by using it to tackle the cold start problem in graph active learning
|
[
"['Junwei Su' 'Chuan Wu']"
] |
null | null |
2403.04484
| null | null |
http://arxiv.org/pdf/2403.04484v1
|
2024-03-07T13:36:15Z
|
2024-03-07T13:36:15Z
|
Source Matters: Source Dataset Impact on Model Robustness in Medical
Imaging
|
Transfer learning has become an essential part of medical imaging classification algorithms, often leveraging ImageNet weights. However, the domain shift from natural to medical images has prompted alternatives such as RadImageNet, often demonstrating comparable classification performance. However, it remains unclear whether the performance gains from transfer learning stem from improved generalization or shortcut learning. To address this, we investigate potential confounders -- whether synthetic or sampled from the data -- across two publicly available chest X-ray and CT datasets. We show that ImageNet and RadImageNet achieve comparable classification performance, yet ImageNet is much more prone to overfitting to confounders. We recommend that researchers using ImageNet-pretrained models reexamine their model robustness by conducting similar experiments. Our code and experiments are available at https://github.com/DovileDo/source-matters.
|
[
"['Dovile Juodelyte' 'Yucheng Lu' 'Amelia Jiménez-Sánchez'\n 'Sabrina Bottazzi' 'Enzo Ferrante' 'Veronika Cheplygina']"
] |
null | null |
2403.04493
| null | null |
http://arxiv.org/pdf/2403.04493v4
|
2024-05-21T14:44:46Z
|
2024-03-07T13:49:43Z
|
What makes an image realistic?
|
The last decade has seen tremendous progress in our ability to generate realistic-looking data, be it images, text, audio, or video. Here, we discuss the closely related problem of quantifying realism, that is, designing functions that can reliably tell realistic data from unrealistic data. This problem turns out to be significantly harder to solve and remains poorly understood, despite its prevalence in machine learning and recent breakthroughs in generative AI. Drawing on insights from algorithmic information theory, we discuss why this problem is challenging, why a good generative model alone is insufficient to solve it, and what a good solution would look like. In particular, we introduce the notion of a universal critic, which unlike adversarial critics does not require adversarial training. While universal critics are not immediately practical, they can serve both as a North Star for guiding practical implementations and as a tool for analyzing existing attempts to capture realism.
|
[
"['Lucas Theis']"
] |
null | null |
2403.04523
| null | null |
http://arxiv.org/pdf/2403.04523v1
|
2024-03-07T14:25:03Z
|
2024-03-07T14:25:03Z
|
T-TAME: Trainable Attention Mechanism for Explaining Convolutional
Networks and Vision Transformers
|
The development and adoption of Vision Transformers and other deep-learning architectures for image classification tasks has been rapid. However, the "black box" nature of neural networks is a barrier to adoption in applications where explainability is essential. While some techniques for generating explanations have been proposed, primarily for Convolutional Neural Networks, adapting such techniques to the new paradigm of Vision Transformers is non-trivial. This paper presents T-TAME, Transformer-compatible Trainable Attention Mechanism for Explanations, a general methodology for explaining deep neural networks used in image classification tasks. The proposed architecture and training technique can be easily applied to any convolutional or Vision Transformer-like neural network, using a streamlined training approach. After training, explanation maps can be computed in a single forward pass; these explanation maps are comparable to or outperform the outputs of computationally expensive perturbation-based explainability techniques, achieving SOTA performance. We apply T-TAME to three popular deep learning classifier architectures, VGG-16, ResNet-50, and ViT-B-16, trained on the ImageNet dataset, and we demonstrate improvements over existing state-of-the-art explainability methods. A detailed analysis of the results and an ablation study provide insights into how the T-TAME design choices affect the quality of the generated explanation maps.
|
[
"['Mariano V. Ntrougkas' 'Nikolaos Gkalelis' 'Vasileios Mezaris']"
] |
null | null |
2403.04526
| null | null |
http://arxiv.org/pdf/2403.04526v1
|
2024-03-07T14:27:08Z
|
2024-03-07T14:27:08Z
|
Hyperspectral unmixing for Raman spectroscopy via physics-constrained
autoencoders
|
Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a non-destructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.
|
[
"['Dimitar Georgiev' 'Álvaro Fernández-Galiana' 'Simon Vilms Pedersen'\n 'Georgios Papadopoulos' 'Ruoxiao Xie' 'Molly M. Stevens'\n 'Mauricio Barahona']"
] |
null | null |
2403.04529
| null | null |
http://arxiv.org/pdf/2403.04529v1
|
2024-03-07T14:28:04Z
|
2024-03-07T14:28:04Z
|
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
|
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research. To further scale up, it is crucial to incorporate collaboration among multiple specialized and high-quality private domain data sources. However, the challenge of training models locally without sharing private data presents numerous obstacles in data quality control. To tackle this issue, we propose a data quality control pipeline for federated fine-tuning of foundation models. This pipeline computes scores reflecting the quality of training data and determines a global threshold for a unified standard, aiming for improved global performance. Our experiments show that the proposed quality control pipeline facilitates the effectiveness and reliability of the model training, leading to better performance.
|
[
"['Wanru Zhao' 'Yaxin Du' 'Nicholas Donald Lane' 'Siheng Chen'\n 'Yanfeng Wang']"
] |
null | null |
2403.04545
| null | null |
http://arxiv.org/pdf/2403.04545v1
|
2024-03-07T14:40:53Z
|
2024-03-07T14:40:53Z
|
Improve Generalization Ability of Deep Wide Residual Network with A
Suitable Scaling Factor
|
Deep Residual Neural Networks (ResNets) have demonstrated remarkable success across a wide range of real-world applications. In this paper, we identify a suitable scaling factor (denoted by $alpha$) on the residual branch of deep wide ResNets to achieve good generalization ability. We show that if $alpha$ is a constant, the class of functions induced by Residual Neural Tangent Kernel (RNTK) is asymptotically not learnable, as the depth goes to infinity. We also highlight a surprising phenomenon: even if we allow $alpha$ to decrease with increasing depth $L$, the degeneration phenomenon may still occur. However, when $alpha$ decreases rapidly with $L$, the kernel regression with deep RNTK with early stopping can achieve the minimax rate provided that the target regression function falls in the reproducing kernel Hilbert space associated with the infinite-depth RNTK. Our simulation studies on synthetic data and real classification tasks such as MNIST, CIFAR10 and CIFAR100 support our theoretical criteria for choosing $alpha$.
|
[
"['Songtao Tian' 'Zixiong Yu']"
] |
null | null |
2403.04546
| null | null |
http://arxiv.org/pdf/2403.04546v2
|
2024-06-14T14:25:29Z
|
2024-03-07T14:42:33Z
|
Architectural Blueprint For Heterogeneity-Resilient Federated Learning
|
This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture capability to manage non IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies.
|
[
"['Satwat Bashir' 'Tasos Dagiuklas' 'Kasra Kassai' 'Muddesar Iqbal']"
] |
null | null |
2403.04547
| null | null |
http://arxiv.org/pdf/2403.04547v1
|
2024-03-07T14:43:17Z
|
2024-03-07T14:43:17Z
|
CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?
|
We study the effectiveness of data-balancing for mitigating biases in contrastive language-image pretraining (CLIP), identifying areas of strength and limitation. First, we reaffirm prior conclusions that CLIP models can inadvertently absorb societal stereotypes. To counter this, we present a novel algorithm, called Multi-Modal Moment Matching (M4), designed to reduce both representation and association biases (i.e. in first- and second-order statistics) in multimodal data. We use M4 to conduct an in-depth analysis taking into account various factors, such as the model, representation, and data size. Our study also explores the dynamic nature of how CLIP learns and unlearns biases. In particular, we find that fine-tuning is effective in countering representation biases, though its impact diminishes for association biases. Also, data balancing has a mixed impact on quality: it tends to improve classification but can hurt retrieval. Interestingly, data and architectural improvements seem to mitigate the negative impact of data balancing on performance; e.g. applying M4 to SigLIP-B/16 with data quality filters improves COCO image-to-text retrieval @5 from 86% (without data balancing) to 87% and ImageNet 0-shot classification from 77% to 77.5%! Finally, we conclude with recommendations for improving the efficacy of data balancing in multimodal systems.
|
[
"['Ibrahim Alabdulmohsin' 'Xiao Wang' 'Andreas Steiner' 'Priya Goyal'\n \"Alexander D'Amour\" 'Xiaohua Zhai']"
] |
null | null |
2403.04551
| null | null |
http://arxiv.org/pdf/2403.04551v1
|
2024-03-07T14:45:03Z
|
2024-03-07T14:45:03Z
|
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness
Characterization Methods for Data-Centric AI
|
Characterizing samples that are difficult to learn from is crucial to developing highly performant ML models. This has led to numerous Hardness Characterization Methods (HCMs) that aim to identify "hard" samples. However, there is a lack of consensus regarding the definition and evaluation of "hardness". Unfortunately, current HCMs have only been evaluated on specific types of hardness and often only qualitatively or with respect to downstream performance, overlooking the fundamental quantitative identification task. We address this gap by presenting a fine-grained taxonomy of hardness types. Additionally, we propose the Hardness Characterization Analysis Toolkit (H-CAT), which supports comprehensive and quantitative benchmarking of HCMs across the hardness taxonomy and can easily be extended to new HCMs, hardness types, and datasets. We use H-CAT to evaluate 13 different HCMs across 8 hardness types. This comprehensive evaluation encompassing over 14K setups uncovers strengths and weaknesses of different HCMs, leading to practical tips to guide HCM selection and future development. Our findings highlight the need for more comprehensive HCM evaluation, while we hope our hardness taxonomy and toolkit will advance the principled evaluation and uptake of data-centric AI methods.
|
[
"['Nabeel Seedat' 'Fergus Imrie' 'Mihaela van der Schaar']"
] |
null | null |
2403.04553
| null | null |
http://arxiv.org/pdf/2403.04553v1
|
2024-03-07T14:48:48Z
|
2024-03-07T14:48:48Z
|
Improvements & Evaluations on the MLCommons CloudMask Benchmark
|
In this paper, we report the performance benchmarking results of deep learning models on MLCommons' Science cloud-masking benchmark using a high-performance computing cluster at New York University (NYU): NYU Greene. MLCommons is a consortium that develops and maintains several scientific benchmarks that can benefit from developments in AI. We provide a description of the cloud-masking benchmark task, updated code, and the best model for this benchmark when using our selected hyperparameter settings. Our benchmarking results include the highest accuracy achieved on the NYU system as well as the average time taken for both training and inference on the benchmark across several runs/seeds. Our code can be found on GitHub. MLCommons team has been kept informed about our progress and may use the developed code for their future work.
|
[
"['Varshitha Chennamsetti' 'Laiba Mehnaz' 'Dan Zhao' 'Banani Ghosh'\n 'Sergey V. Samsonau']"
] |
null | null |
2403.04558
| null | null |
http://arxiv.org/pdf/2403.04558v2
|
2024-03-12T11:42:06Z
|
2024-03-07T14:56:06Z
|
Reducing self-supervised learning complexity improves weakly-supervised
classification performance in computational pathology
|
Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations performed by clinicians, which are scarce and costly to generate. The emergence of self-supervised learning (SSL) methods remove this barrier, allowing for large-scale analyses on non-annotated data. However, recent SSL approaches apply increasingly expansive model architectures and larger datasets, causing the rapid escalation of data volumes, hardware prerequisites, and overall expenses, limiting access to these resources to few institutions. Therefore, we investigated the complexity of contrastive SSL in computational pathology in relation to classification performance with the utilization of consumer-grade hardware. Specifically, we analyzed the effects of adaptations in data volume, architecture, and algorithms on downstream classification tasks, emphasizing their impact on computational resources. We trained breast cancer foundation models on a large public patient cohort and validated them on various downstream classification tasks in a weakly supervised manner on two external public patient cohorts. Our experiments demonstrate that we can improve downstream classification performance whilst reducing SSL training duration by 90%. In summary, we propose a set of adaptations which enable the utilization of SSL in computational pathology in non-resource abundant environments.
|
[
"['Tim Lenz' 'Omar S. M. El Nahhas' 'Marta Ligero' 'Jakob Nikolas Kather']"
] |
null | null |
2403.04568
| null | null |
http://arxiv.org/pdf/2403.04568v1
|
2024-03-07T15:03:50Z
|
2024-03-07T15:03:50Z
|
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit
Feedback and Unknown Transition
|
We study reinforcement learning with linear function approximation, unknown transition, and adversarial losses in the bandit feedback setting. Specifically, we focus on linear mixture MDPs whose transition kernel is a linear mixture model. We propose a new algorithm that attains an $widetilde{O}(dsqrt{HS^3K} + sqrt{HSAK})$ regret with high probability, where $d$ is the dimension of feature mappings, $S$ is the size of state space, $A$ is the size of action space, $H$ is the episode length and $K$ is the number of episodes. Our result strictly improves the previous best-known $widetilde{O}(dS^2 sqrt{K} + sqrt{HSAK})$ result in Zhao et al. (2023a) since $H leq S$ holds by the layered MDP structure. Our advancements are primarily attributed to (i) a new least square estimator for the transition parameter that leverages the visit information of all states, as opposed to only one state in prior work, and (ii) a new self-normalized concentration tailored specifically to handle non-independent noises, originally proposed in the dynamic assortment area and firstly applied in reinforcement learning to handle correlations between different states.
|
[
"['Long-Fei Li' 'Peng Zhao' 'Zhi-Hua Zhou']"
] |
null | null |
2403.04580
| null | null |
http://arxiv.org/pdf/2403.04580v1
|
2024-03-07T15:26:23Z
|
2024-03-07T15:26:23Z
|
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with
Machine Learning Models Trained on a Large-Scale Mechanistic Dataset
|
Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation.
|
[
"['Joonyoung F. Joung' 'Mun Hong Fong' 'Jihye Roh' 'Zhengkai Tu'\n 'John Bradshaw' 'Connor W. Coley']"
] |
null | null |
2403.04586
| null | null |
http://arxiv.org/pdf/2403.04586v2
|
2024-07-10T11:57:01Z
|
2024-03-07T15:30:54Z
|
Learning Speed Adaptation for Flight in Clutter
|
Animals learn to adapt speed of their movements to their capabilities and the environment they observe. Mobile robots should also demonstrate this ability to trade-off aggressiveness and safety for efficiently accomplishing tasks. The aim of this work is to endow flight vehicles with the ability of speed adaptation in prior unknown and partially observable cluttered environments. We propose a hierarchical learning and planning framework where we utilize both well-established methods of model-based trajectory generation and trial-and-error that comprehensively learns a policy to dynamically configure the speed constraint. Technically, we use online reinforcement learning to obtain the deployable policy. The statistical results in simulation demonstrate the advantages of our method over the constant speed constraint baselines and an alternative method in terms of flight efficiency and safety. In particular, the policy behaves perception awareness, which distinguish it from alternative approaches. By deploying the policy to hardware, we verify that these advantages can be brought to the real world.
|
[
"['Guangyu Zhao' 'Tianyue Wu' 'Yeke Chen' 'Fei Gao']"
] |
null | null |
2403.04599
| null | null |
http://arxiv.org/pdf/2403.04599v1
|
2024-03-07T15:47:52Z
|
2024-03-07T15:47:52Z
|
Contrastive Continual Learning with Importance Sampling and
Prototype-Instance Relation Distillation
|
Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://github.com/lijy373/CCLIS.
|
[
"['Jiyong Li' 'Dilshod Azizov' 'Yang Li' 'Shangsong Liang']"
] |
null | null |
2403.04605
| null | null |
http://arxiv.org/pdf/2403.04605v2
|
2024-03-08T14:49:34Z
|
2024-03-07T15:54:46Z
|
In-n-Out: Calibrating Graph Neural Networks for Link Prediction
|
Deep neural networks are notoriously miscalibrated, i.e., their outputs do not reflect the true probability of the event we aim to predict. While networks for tabular or image data are usually overconfident, recent works have shown that graph neural networks (GNNs) show the opposite behavior for node-level classification. But what happens when we are predicting links? We show that, in this case, GNNs often exhibit a mixed behavior. More specifically, they may be overconfident in negative predictions while being underconfident in positive ones. Based on this observation, we propose IN-N-OUT, the first-ever method to calibrate GNNs for link prediction. IN-N-OUT is based on two simple intuitions: i) attributing true/false labels to an edge while respecting a GNNs prediction should cause but small fluctuations in that edge's embedding; and, conversely, ii) if we label that same edge contradicting our GNN, embeddings should change more substantially. An extensive experimental campaign shows that IN-N-OUT significantly improves the calibration of GNNs in link prediction, consistently outperforming the baselines available -- which are not designed for this specific task.
|
[
"['Erik Nascimento' 'Diego Mesquita' 'Samuel Kaski' 'Amauri H Souza']"
] |
null | null |
2403.04626
| null | null |
http://arxiv.org/pdf/2403.04626v2
|
2024-05-31T00:12:59Z
|
2024-03-07T16:11:43Z
|
MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training
with Masked Autoencoder
|
Within the domain of medical analysis, extensive research has explored the potential of mutual learning between Masked Autoencoders(MAEs) and multimodal data. However, the impact of MAEs on intermodality remains a key challenge. We introduce MedFLIP, a Fast Language-Image Pre-training method for Medical analysis. We explore MAEs for zero-shot learning with crossed domains, which enhances the model's ability to learn from limited data, a common scenario in medical diagnostics. We verify that masking an image does not affect inter-modal learning. Furthermore, we propose the SVD loss to enhance the representation learning for characteristics of medical images, aiming to improve classification accuracy by leveraging the structural intricacies of such data. Our theory posits that masking encourages semantic preservation, robust feature extraction, regularization, domain adaptation, and invariance learning. Lastly, we validate using language will improve the zero-shot performance for the medical image analysis. MedFLIP's scaling of the masking process marks an advancement in the field, offering a pathway to rapid and precise medical image analysis without the traditional computational bottlenecks. Through experiments and validation, MedFLIP demonstrates efficient performance improvements, helps for future research and application in medical diagnostics.
|
[
"['Lei Li' 'Tianfang Zhang' 'Xinglin Zhang' 'Jiaqi Liu' 'Bingqi Ma'\n 'Yan Luo' 'Tao Chen']"
] |
null | null |
2403.04629
| null | null |
http://arxiv.org/pdf/2403.04629v2
|
2024-03-08T07:52:32Z
|
2024-03-07T16:13:32Z
|
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI
Collaboration
|
Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often considered a black box itself, lacking ways to provide reasons as to why certain parameters are proposed to be evaluated. This is particularly relevant in human-in-the-loop applications of BO, such as in robotics. We address this issue by proposing ShapleyBO, a framework for interpreting BO's proposals by game-theoretic Shapley values.They quantify each parameter's contribution to BO's acquisition function. Exploiting the linearity of Shapley values, we are further able to identify how strongly each parameter drives BO's exploration and exploitation for additive acquisition functions like the confidence bound. We also show that ShapleyBO can disentangle the contributions to exploration into those that explore aleatoric and epistemic uncertainty. Moreover, our method gives rise to a ShapleyBO-assisted human machine interface (HMI), allowing users to interfere with BO in case proposals do not align with human reasoning. We demonstrate this HMI's benefits for the use case of personalizing wearable robotic devices (assistive back exosuits) by human-in-the-loop BO. Results suggest human-BO teams with access to ShapleyBO can achieve lower regret than teams without.
|
[
"['Julian Rodemann' 'Federico Croppi' 'Philipp Arens' 'Yusuf Sale'\n 'Julia Herbinger' 'Bernd Bischl' 'Eyke Hüllermeier' 'Thomas Augustin'\n 'Conor J. Walsh' 'Giuseppe Casalicchio']"
] |
null | null |
2403.04636
| null | null |
http://arxiv.org/pdf/2403.04636v1
|
2024-03-07T16:21:09Z
|
2024-03-07T16:21:09Z
|
Entropy Aware Message Passing in Graph Neural Networks
|
Deep Graph Neural Networks struggle with oversmoothing. This paper introduces a novel, physics-inspired GNN model designed to mitigate this issue. Our approach integrates with existing GNN architectures, introducing an entropy-aware message passing term. This term performs gradient ascent on the entropy during node aggregation, thereby preserving a certain degree of entropy in the embeddings. We conduct a comparative analysis of our model against state-of-the-art GNNs across various common datasets.
|
[
"['Philipp Nazari' 'Oliver Lemke' 'Davide Guidobene' 'Artiom Gesp']"
] |
null | null |
2403.04642
| null | null |
http://arxiv.org/pdf/2403.04642v1
|
2024-03-07T16:36:29Z
|
2024-03-07T16:36:29Z
|
Teaching Large Language Models to Reason with Reinforcement Learning
|
Reinforcement Learning from Human Feedback (textbf{RLHF}) has emerged as a dominant approach for aligning LLM outputs with human preferences. Inspired by the success of RLHF, we study the performance of multiple algorithms that learn from feedback (Expert Iteration, Proximal Policy Optimization (textbf{PPO}), Return-Conditioned RL) on improving LLM reasoning capabilities. We investigate both sparse and dense rewards provided to the LLM both heuristically and via a learned reward model. We additionally start from multiple model sizes and initializations both with and without supervised fine-tuning (textbf{SFT}) data. Overall, we find all algorithms perform comparably, with Expert Iteration performing best in most cases. Surprisingly, we find the sample complexity of Expert Iteration is similar to that of PPO, requiring at most on the order of $10^6$ samples to converge from a pretrained checkpoint. We investigate why this is the case, concluding that during RL training models fail to explore significantly beyond solutions already produced by SFT models. Additionally, we discuss a trade off between maj@1 and pass@96 metric performance during SFT training and how conversely RL training improves both simultaneously. We then conclude by discussing the implications of our findings for RLHF and the future role of RL in LLM fine-tuning.
|
[
"['Alex Havrilla' 'Yuqing Du' 'Sharath Chandra Raparthy'\n 'Christoforos Nalmpantis' 'Jane Dwivedi-Yu' 'Maksym Zhuravinskyi'\n 'Eric Hambro' 'Sainbayar Sukhbaatar' 'Roberta Raileanu']"
] |
null | null |
2403.04650
| null | null |
http://arxiv.org/pdf/2403.04650v2
|
2024-03-08T14:29:41Z
|
2024-03-07T16:50:25Z
|
Context-Based Multimodal Fusion
|
The fusion models, which effectively combine information from different sources, are widely used in solving multimodal tasks. However, they have significant limitations related to aligning data distributions across different modalities. This challenge can lead to inconsistencies and difficulties in learning robust representations. Alignment models, while specifically addressing this issue, often require training "from scratch" with large datasets to achieve optimal results, which can be costly in terms of resources and time. To overcome these limitations, we propose an innovative model called Context-Based Multimodal Fusion (CBMF), which combines both modality fusion and data distribution alignment. In CBMF, each modality is represented by a specific context vector, fused with the embedding of each modality. This enables the use of large pre-trained models that can be frozen, reducing the computational and training data requirements. Additionally, the network learns to differentiate embeddings of different modalities through fusion with context and aligns data distributions using a contrastive approach for self-supervised learning. Thus, CBMF offers an effective and economical solution for solving complex multimodal tasks.
|
[
"['Bilal Faye' 'Hanane Azzag' 'Mustapha Lebbah' 'Djamel Bouchaffra']"
] |
null | null |
2403.04661
| null | null |
http://arxiv.org/pdf/2403.04661v3
|
2024-04-22T14:04:55Z
|
2024-03-07T17:07:51Z
|
Dynamic Cross Attention for Audio-Visual Person Verification
|
Although person or identity verification has been predominantly explored using individual modalities such as face and voice, audio-visual fusion has recently shown immense potential to outperform unimodal approaches. Audio and visual modalities are often expected to pose strong complementary relationships, which plays a crucial role in effective audio-visual fusion. However, they may not always strongly complement each other, they may also exhibit weak complementary relationships, resulting in poor audio-visual feature representations. In this paper, we propose a Dynamic Cross-Attention (DCA) model that can dynamically select the cross-attended or unattended features on the fly based on the strong or weak complementary relationships, respectively, across audio and visual modalities. In particular, a conditional gating layer is designed to evaluate the contribution of the cross-attention mechanism and choose cross-attended features only when they exhibit strong complementary relationships, otherwise unattended features. Extensive experiments are conducted on the Voxceleb1 dataset to demonstrate the robustness of the proposed model. Results indicate that the proposed model consistently improves the performance on multiple variants of cross-attention while outperforming the state-of-the-art methods.
|
[
"['R. Gnana Praveen' 'Jahangir Alam']"
] |
null | null |
2403.04666
| null | null |
http://arxiv.org/pdf/2403.04666v2
|
2024-06-25T09:28:43Z
|
2024-03-07T17:13:12Z
|
Telecom Language Models: Must They Be Large?
|
The increasing interest in Large Language Models (LLMs) within the telecommunications sector underscores their potential to revolutionize operational efficiency. However, the deployment of these sophisticated models is often hampered by their substantial size and computational demands, raising concerns about their viability in resource-constrained environments. Addressing this challenge, recent advancements have seen the emergence of small language models that surprisingly exhibit performance comparable to their larger counterparts in many tasks, such as coding and common-sense reasoning. Phi-2, a compact yet powerful model, exemplifies this new wave of efficient small language models. This paper conducts a comprehensive evaluation of Phi-2's intrinsic understanding of the telecommunications domain. Recognizing the scale-related limitations, we enhance Phi-2's capabilities through a Retrieval-Augmented Generation approach, meticulously integrating an extensive knowledge base specifically curated with telecom standard specifications. The enhanced Phi-2 model demonstrates a profound improvement in accuracy, answering questions about telecom standards with a precision that closely rivals the more resource-intensive GPT-3.5. The paper further explores the refined capabilities of Phi-2 in addressing problem-solving scenarios within the telecom sector, highlighting its potential and limitations.
|
[
"['Nicola Piovesan' 'Antonio De Domenico' 'Fadhel Ayed']"
] |
null | null |
2403.04670
| null | null |
http://arxiv.org/pdf/2403.04670v1
|
2024-03-07T17:16:59Z
|
2024-03-07T17:16:59Z
|
End-to-end Conditional Robust Optimization
|
The field of Contextual Optimization (CO) integrates machine learning and optimization to solve decision making problems under uncertainty. Recently, a risk sensitive variant of CO, known as Conditional Robust Optimization (CRO), combines uncertainty quantification with robust optimization in order to promote safety and reliability in high stake applications. Exploiting modern differentiable optimization methods, we propose a novel end-to-end approach to train a CRO model in a way that accounts for both the empirical risk of the prescribed decisions and the quality of conditional coverage of the contextual uncertainty set that supports them. While guarantees of success for the latter objective are impossible to obtain from the point of view of conformal prediction theory, high quality conditional coverage is achieved empirically by ingeniously employing a logistic regression differentiable layer within the calculation of coverage quality in our training loss. We show that the proposed training algorithms produce decisions that outperform the traditional estimate then optimize approaches.
|
[
"['Abhilash Chenreddy' 'Erick Delage']"
] |
null | null |
2403.04690
| null | null |
http://arxiv.org/pdf/2403.04690v2
|
2024-03-22T16:26:40Z
|
2024-03-07T17:35:58Z
|
Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self
Attention at the Threadblock Level
|
Neighborhood attention reduces the cost of self attention by restricting each token's attention span to its nearest neighbors. This restriction, parameterized by a window size and dilation factor, draws a spectrum of possible attention patterns between linear projection and self attention. Neighborhood attention, and more generally sliding window attention patterns, have long been bounded by infrastructure, particularly in higher-rank spaces (2-D and 3-D), calling for the development of custom kernels, which have been limited in either functionality, or performance, if not both. In this work, we first show that neighborhood attention can be represented as a batched GEMM problem, similar to standard attention, and implement it for 1-D and 2-D neighborhood attention. These kernels on average provide 895% and 272% improvement in full precision latency compared to existing naive kernels for 1-D and 2-D neighborhood attention respectively. We find certain inherent inefficiencies in all unfused neighborhood attention kernels that bound their performance and lower-precision scalability. We also developed fused neighborhood attention; an adaptation of fused dot-product attention kernels that allow fine-grained control over attention across different spatial axes. Known for reducing the quadratic time complexity of self attention to a linear complexity, neighborhood attention can now enjoy a reduced and constant memory footprint, and record-breaking half precision latency. We observe that our fused kernels successfully circumvent some of the unavoidable inefficiencies in unfused implementations. While our unfused GEMM-based kernels only improve half precision performance compared to naive kernels by an average of 496% and 113% in 1-D and 2-D problems respectively, our fused kernels improve naive kernels by an average of 1607% and 581% in 1-D and 2-D problems respectively.
|
[
"['Ali Hassani' 'Wen-Mei Hwu' 'Humphrey Shi']"
] |
null | null |
2403.04693
| null | null |
http://arxiv.org/pdf/2403.04693v1
|
2024-03-07T17:42:40Z
|
2024-03-07T17:42:40Z
|
Analysis of Systems' Performance in Natural Language Processing
Competitions
|
Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants receive a training set and are expected to provide a solution for a held-out dataset kept by organizers. An essential challenge for organizers arises when comparing algorithms' performance, assessing multiple participants, and ranking them. Statistical tools are often used for this purpose; however, traditional statistical methods often fail to capture decisive differences between systems' performance. This manuscript describes an evaluation methodology for statistically analyzing competition results and competition. The methodology is designed to be universally applicable; however, it is illustrated using eight natural language competitions as case studies involving classification and regression problems. The proposed methodology offers several advantages, including off-the-shell comparisons with correction mechanisms and the inclusion of confidence intervals. Furthermore, we introduce metrics that allow organizers to assess the difficulty of competitions. Our analysis shows the potential usefulness of our methodology for effectively evaluating competition results.
|
[
"['Sergio Nava-Muñoz' 'Mario Graff' 'Hugo Jair Escalante']"
] |
null | null |
2403.04696
| null | null |
http://arxiv.org/pdf/2403.04696v2
|
2024-06-06T21:32:39Z
|
2024-03-07T17:44:17Z
|
Fact-Checking the Output of Large Language Models via Token-Level
Uncertainty Quantification
|
Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccuracies in the generated text might be obscured by the rest of the output being generally factually correct, making it extremely hard for the users to spot them. Current services that leverage LLMs usually do not provide any means for detecting unreliable generations. Here, we aim to bridge this gap. In particular, we propose a novel fact-checking and hallucination detection pipeline based on token-level uncertainty quantification. Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output. Moreover, we present a novel token-level uncertainty quantification method that removes the impact of uncertainty about what claim to generate on the current step and what surface form to use. Our method Claim Conditioned Probability (CCP) measures only the uncertainty of a particular claim value expressed by the model. Experiments on the task of biography generation demonstrate strong improvements for CCP compared to the baselines for seven LLMs and four languages. Human evaluation reveals that the fact-checking pipeline based on uncertainty quantification is competitive with a fact-checking tool that leverages external knowledge.
|
[
"['Ekaterina Fadeeva' 'Aleksandr Rubashevskii' 'Artem Shelmanov'\n 'Sergey Petrakov' 'Haonan Li' 'Hamdy Mubarak' 'Evgenii Tsymbalov'\n 'Gleb Kuzmin' 'Alexander Panchenko' 'Timothy Baldwin' 'Preslav Nakov'\n 'Maxim Panov']"
] |
null | null |
2403.04720
| null | null |
http://arxiv.org/pdf/2403.04720v3
|
2024-05-26T11:38:24Z
|
2024-03-07T18:16:29Z
|
Rethinking of Encoder-based Warm-start Methods in Hyperparameter
Optimization
|
Effectively representing heterogeneous tabular datasets for meta-learning remains an open problem. Previous approaches rely on predefined meta-features, for example, statistical measures or landmarkers. Encoder-based models, such as Dataset2Vec, allow us to extract significant meta-features automatically without human intervention. This research introduces a novel encoder-based representation of tabular datasets implemented within the liltab package available on GitHub https://github.com/azoz01/liltab. Our package is based on an established model for heterogeneous tabular data proposed in [Tomoharu Iwata and Atsutoshi Kumagai. Meta-learning from Tasks with Heterogeneous Attribute Spaces. In Advances in Neural Information Processing Systems, 2020]. The proposed approach employs a different model for encoding feature relationships, generating alternative representations compared to existing methods like Dataset2Vec. Both of them leverage the fundamental assumption of dataset similarity learning. In this work, we evaluate Dataset2Vec and liltab on two common meta-tasks -- representing entire datasets and hyperparameter optimization warm-start. However, validation on an independent metaMIMIC dataset highlights the nuanced challenges in representation learning. We show that general representations may not suffice for some meta-tasks where requirements are not explicitly considered during extraction.
|
[
"['Dawid Płudowski' 'Antoni Zajko' 'Anna Kozak' 'Katarzyna Woźnica']"
] |
null | null |
2403.04726
| null | null |
http://arxiv.org/pdf/2403.04726v1
|
2024-03-07T18:23:51Z
|
2024-03-07T18:23:51Z
|
A Sub-Quadratic Time Algorithm for Robust Sparse Mean Estimation
|
We study the algorithmic problem of sparse mean estimation in the presence of adversarial outliers. Specifically, the algorithm observes a emph{corrupted} set of samples from $mathcal{N}(mu,mathbf{I}_d)$, where the unknown mean $mu in mathbb{R}^d$ is constrained to be $k$-sparse. A series of prior works has developed efficient algorithms for robust sparse mean estimation with sample complexity $mathrm{poly}(k,log d, 1/epsilon)$ and runtime $d^2 mathrm{poly}(k,log d,1/epsilon)$, where $epsilon$ is the fraction of contamination. In particular, the fastest runtime of existing algorithms is quadratic ($Omega(d^2)$), which can be prohibitive in high dimensions. This quadratic barrier in the runtime stems from the reliance of these algorithms on the sample covariance matrix, which is of size $d^2$. Our main contribution is an algorithm for robust sparse mean estimation which runs in emph{subquadratic} time using $mathrm{poly}(k,log d,1/epsilon)$ samples. We also provide analogous results for robust sparse PCA. Our results build on algorithmic advances in detecting weak correlations, a generalized version of the light-bulb problem by Valiant.
|
[
"['Ankit Pensia']"
] |
null | null |
2403.04744
| null | null |
http://arxiv.org/pdf/2403.04744v1
|
2024-03-07T18:49:32Z
|
2024-03-07T18:49:32Z
|
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker
Assumptions
|
We study the complexity of Non-Gaussian Component Analysis (NGCA) in the Statistical Query (SQ) model. Prior work developed a general methodology to prove SQ lower bounds for this task that have been applicable to a wide range of contexts. In particular, it was known that for any univariate distribution $A$ satisfying certain conditions, distinguishing between a standard multivariate Gaussian and a distribution that behaves like $A$ in a random hidden direction and like a standard Gaussian in the orthogonal complement, is SQ-hard. The required conditions were that (1) $A$ matches many low-order moments with the standard univariate Gaussian, and (2) the chi-squared norm of $A$ with respect to the standard Gaussian is finite. While the moment-matching condition is necessary for hardness, the chi-squared condition was only required for technical reasons. In this work, we establish that the latter condition is indeed not necessary. In particular, we prove near-optimal SQ lower bounds for NGCA under the moment-matching condition only. Our result naturally generalizes to the setting of a hidden subspace. Leveraging our general SQ lower bound, we obtain near-optimal SQ lower bounds for a range of concrete estimation tasks where existing techniques provide sub-optimal or even vacuous guarantees.
|
[
"['Ilias Diakonikolas' 'Daniel Kane' 'Lisheng Ren' 'Yuxin Sun']"
] |
null | null |
2403.04746
| null | null |
http://arxiv.org/pdf/2403.04746v1
|
2024-03-07T18:50:51Z
|
2024-03-07T18:50:51Z
|
LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
|
Tools are essential for large language models (LLMs) to acquire up-to-date information and take consequential actions in external environments. Existing work on tool-augmented LLMs primarily focuses on the broad coverage of tools and the flexibility of adding new tools. However, a critical aspect that has surprisingly been understudied is simply how accurately an LLM uses tools for which it has been trained. We find that existing LLMs, including GPT-4 and open-source LLMs specifically fine-tuned for tool use, only reach a correctness rate in the range of 30% to 60%, far from reliable use in practice. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE), that orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory. Specifically, STE leverages an LLM's 'imagination' to simulate plausible scenarios for using a tool, after which the LLM interacts with the tool to learn from its execution feedback. Both short-term and long-term memory are employed to improve the depth and breadth of the exploration, respectively. Comprehensive experiments on ToolBench show that STE substantially improves tool learning for LLMs under both in-context learning and fine-tuning settings, bringing a boost of 46.7% to Mistral-Instruct-7B and enabling it to outperform GPT-4. We also show effective continual learning of tools via a simple experience replay strategy.
|
[
"['Boshi Wang' 'Hao Fang' 'Jason Eisner' 'Benjamin Van Durme' 'Yu Su']"
] |
null | null |
2403.04747
| null | null |
http://arxiv.org/pdf/2403.04747v1
|
2024-03-07T18:52:27Z
|
2024-03-07T18:52:27Z
|
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural
Networks
|
Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling. The expressivity of GNNs with respect to their ability to discriminate non-isomorphic graphs critically depends on the functions employed for message aggregation and graph-level readout. By applying signal propagation theory, we propose a variance-preserving aggregation function (VPA) that maintains expressivity, but yields improved forward and backward dynamics. Experiments demonstrate that VPA leads to increased predictive performance for popular GNN architectures as well as improved learning dynamics. Our results could pave the way towards normalizer-free or self-normalizing GNNs.
|
[
"['Lisa Schneckenreiter' 'Richard Freinschlag' 'Florian Sestak'\n 'Johannes Brandstetter' 'Günter Klambauer' 'Andreas Mayr']"
] |
null | null |
2403.04750
| null | null |
http://arxiv.org/pdf/2403.04750v2
|
2024-07-07T17:53:28Z
|
2024-03-07T18:53:53Z
|
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
|
Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The recent addition of machine learning methods to the toolbox for solving such problems is pushing the boundary of the quality vs. speed tradeoff of such numerical simulations. In this work, we lead the way to Lagrangian fluid simulators compatible with deep learning frameworks, and propose JAX-SPH - a Smoothed Particle Hydrodynamics (SPH) framework implemented in JAX. JAX-SPH builds on the code for dataset generation from the LagrangeBench project (Toshev et al., 2023) and extends this code in multiple ways: (a) integration of further key SPH algorithms, (b) restructuring the code toward a Python package, (c) verification of the gradients through the solver, and (d) demonstration of the utility of the gradients for solving inverse problems as well as a Solver-in-the-Loop application. Our code is available at https://github.com/tumaer/jax-sph.
|
[
"['Artur P. Toshev' 'Harish Ramachandran' 'Jonas A. Erbesdobler'\n 'Gianluca Galletti' 'Johannes Brandstetter' 'Nikolaus A. Adams']"
] |
null | null |
2403.04758
| null | null |
http://arxiv.org/abs/2403.04758v1
|
2024-03-07T18:56:31Z
|
2024-03-07T18:56:31Z
|
KnowledgeVIS: Interpreting Language Models by Comparing
Fill-in-the-Blank Prompts
|
Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVis, a human-in-the-loop visual analytics system for interpreting language models using fill-in-the-blank sentences as prompts. By comparing predictions between sentences, KnowledgeVis reveals learned associations that intuitively connect what language models learn during training to natural language tasks downstream, helping users create and test multiple prompt variations, analyze predicted words using a novel semantic clustering technique, and discover insights using interactive visualizations. Collectively, these visualizations help users identify the likelihood and uniqueness of individual predictions, compare sets of predictions between prompts, and summarize patterns and relationships between predictions across all prompts. We demonstrate the capabilities of KnowledgeVis with feedback from six NLP experts as well as three different use cases: (1) probing biomedical knowledge in two domain-adapted models; and (2) evaluating harmful identity stereotypes and (3) discovering facts and relationships between three general-purpose models.
|
[
"['Adam Coscia' 'Alex Endert']"
] |
null | null |
2403.04759
| null | null |
http://arxiv.org/pdf/2403.04759v1
|
2024-03-07T18:56:33Z
|
2024-03-07T18:56:33Z
|
Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing
|
On-device learning has emerged as a prevailing trend that avoids the slow response time and costly communication of cloud-based learning. The ability to learn continuously and indefinitely in a changing environment, and with resource constraints, is critical for real sensor deployments. However, existing designs are inadequate for practical scenarios with (i) streaming data input, (ii) lack of supervision and (iii) limited on-board resources. In this paper, we design and deploy the first on-device lifelong learning system called LifeHD for general IoT applications with limited supervision. LifeHD is designed based on a novel neurally-inspired and lightweight learning paradigm called Hyperdimensional Computing (HDC). We utilize a two-tier associative memory organization to intelligently store and manage high-dimensional, low-precision vectors, which represent the historical patterns as cluster centroids. We additionally propose two variants of LifeHD to cope with scarce labeled inputs and power constraints. We implement LifeHD on off-the-shelf edge platforms and perform extensive evaluations across three scenarios. Our measurements show that LifeHD improves the unsupervised clustering accuracy by up to 74.8% compared to the state-of-the-art NN-based unsupervised lifelong learning baselines with as much as 34.3x better energy efficiency. Our code is available at https://github.com/Orienfish/LifeHD.
|
[
"['Xiaofan Yu' 'Anthony Thomas' 'Ivannia Gomez Moreno' 'Louis Gutierrez'\n 'Tajana Rosing']"
] |
null | null |
2403.04760
| null | null |
http://arxiv.org/abs/2403.04760v1
|
2024-03-07T18:56:39Z
|
2024-03-07T18:56:39Z
|
iScore: Visual Analytics for Interpreting How Language Models
Automatically Score Summaries
|
The recent explosion in popularity of large language models (LLMs) has inspired learning engineers to incorporate them into adaptive educational tools that automatically score summary writing. Understanding and evaluating LLMs is vital before deploying them in critical learning environments, yet their unprecedented size and expanding number of parameters inhibits transparency and impedes trust when they underperform. Through a collaborative user-centered design process with several learning engineers building and deploying summary scoring LLMs, we characterized fundamental design challenges and goals around interpreting their models, including aggregating large text inputs, tracking score provenance, and scaling LLM interpretability methods. To address their concerns, we developed iScore, an interactive visual analytics tool for learning engineers to upload, score, and compare multiple summaries simultaneously. Tightly integrated views allow users to iteratively revise the language in summaries, track changes in the resulting LLM scores, and visualize model weights at multiple levels of abstraction. To validate our approach, we deployed iScore with three learning engineers over the course of a month. We present a case study where interacting with iScore led a learning engineer to improve their LLM's score accuracy by three percentage points. Finally, we conducted qualitative interviews with the learning engineers that revealed how iScore enabled them to understand, evaluate, and build trust in their LLMs during deployment.
|
[
"['Adam Coscia' 'Langdon Holmes' 'Wesley Morris' 'Joon Suh Choi'\n 'Scott Crossley' 'Alex Endert']"
] |
null | null |
2403.04763
| null | null |
http://arxiv.org/pdf/2403.04763v1
|
2024-03-07T18:57:46Z
|
2024-03-07T18:57:46Z
|
BloomGML: Graph Machine Learning through the Lens of Bilevel
Optimization
|
Bilevel optimization refers to scenarios whereby the optimal solution of a lower-level energy function serves as input features to an upper-level objective of interest. These optimal features typically depend on tunable parameters of the lower-level energy in such a way that the entire bilevel pipeline can be trained end-to-end. Although not generally presented as such, this paper demonstrates how a variety of graph learning techniques can be recast as special cases of bilevel optimization or simplifications thereof. In brief, building on prior work we first derive a more flexible class of energy functions that, when paired with various descent steps (e.g., gradient descent, proximal methods, momentum, etc.), form graph neural network (GNN) message-passing layers; critically, we also carefully unpack where any residual approximation error lies with respect to the underlying constituent message-passing functions. We then probe several simplifications of this framework to derive close connections with non-GNN-based graph learning approaches, including knowledge graph embeddings, various forms of label propagation, and efficient graph-regularized MLP models. And finally, we present supporting empirical results that demonstrate the versatility of the proposed bilevel lens, which we refer to as BloomGML, referencing that BiLevel Optimization Offers More Graph Machine Learning. Our code is available at https://github.com/amberyzheng/BloomGML. Let graph ML bloom.
|
[
"['Amber Yijia Zheng' 'Tong He' 'Yixuan Qiu' 'Minjie Wang' 'David Wipf']"
] |
null | null |
2403.04764
| null | null |
http://arxiv.org/pdf/2403.04764v3
|
2024-05-02T14:16:35Z
|
2024-03-07T18:58:26Z
|
TS-RSR: A provably efficient approach for batch bayesian optimization
|
This paper presents a new approach for batch Bayesian Optimization (BO) called Thompson Sampling-Regret to Sigma Ratio directed sampling (TS-RSR), where we sample a new batch of actions by minimizing a Thompson Sampling approximation of a regret to uncertainty ratio. Our sampling objective is able to coordinate the actions chosen in each batch in a way that minimizes redundancy between points whilst focusing on points with high predictive means or high uncertainty. Theoretically, we provide rigorous convergence guarantees on our algorithm's regret, and numerically, we demonstrate that our method attains state-of-the-art performance on a range of challenging synthetic and realistic test functions, where it outperforms several competitive benchmark batch BO algorithms.
|
[
"['Zhaolin Ren' 'Na Li']"
] |
null | null |
2403.04769
| null | null |
http://arxiv.org/pdf/2403.04769v2
|
2024-03-11T01:21:32Z
|
2024-02-16T17:02:53Z
|
Using Hallucinations to Bypass GPT4's Filter
|
Large language models (LLMs) are initially trained on vast amounts of data, then fine-tuned using reinforcement learning from human feedback (RLHF); this also serves to teach the LLM to provide appropriate and safe responses. In this paper, we present a novel method to manipulate the fine-tuned version into reverting to its pre-RLHF behavior, effectively erasing the model's filters; the exploit currently works for GPT4, Claude Sonnet, and (to some extent) for Inflection-2.5. Unlike other jailbreaks (for example, the popular "Do Anything Now" (DAN) ), our method does not rely on instructing the LLM to override its RLHF policy; hence, simply modifying the RLHF process is unlikely to address it. Instead, we induce a hallucination involving reversed text during which the model reverts to a word bucket, effectively pausing the model's filter. We believe that our exploit presents a fundamental vulnerability in LLMs currently unaddressed, as well as an opportunity to better understand the inner workings of LLMs during hallucinations.
|
[
"['Benjamin Lemkin']"
] |
null | null |
2403.04770
| null | null |
http://arxiv.org/pdf/2403.04770v1
|
2024-02-26T01:55:45Z
|
2024-02-26T01:55:45Z
|
Social Orientation: A New Feature for Dialogue Analysis
|
There are many settings where it is useful to predict and explain the success or failure of a dialogue. Circumplex theory from psychology models the social orientations (e.g., Warm-Agreeable, Arrogant-Calculating) of conversation participants and can be used to predict and explain the outcome of social interactions. Our work is novel in its systematic application of social orientation tags to modeling conversation outcomes. In this paper, we introduce a new data set of dialogue utterances machine-labeled with social orientation tags. We show that social orientation tags improve task performance, especially in low-resource settings, on both English and Chinese language benchmarks. We also demonstrate how social orientation tags help explain the outcomes of social interactions when used in neural models. Based on these results showing the utility of social orientation tags for dialogue outcome prediction tasks, we release our data sets, code, and models that are fine-tuned to predict social orientation tags on dialogue utterances.
|
[
"['Todd Morrill' 'Zhaoyuan Deng' 'Yanda Chen' 'Amith Ananthram'\n 'Colin Wayne Leach' 'Kathleen McKeown']"
] |
null | null |
2403.04778
| null | null |
http://arxiv.org/pdf/2403.04778v3
|
2024-05-01T05:34:53Z
|
2024-03-02T01:05:25Z
|
An Efficient Difference-of-Convex Solver for Privacy Funnel
|
We propose an efficient solver for the privacy funnel (PF) method, leveraging its difference-of-convex (DC) structure. The proposed DC separation results in a closed-form update equation, which allows straightforward application to both known and unknown distribution settings. For known distribution case, we prove the convergence (local stationary points) of the proposed non-greedy solver, and empirically show that it outperforms the state-of-the-art approaches in characterizing the privacy-utility trade-off. The insights of our DC approach apply to unknown distribution settings where labeled empirical samples are available instead. Leveraging the insights, our alternating minimization solver satisfies the fundamental Markov relation of PF in contrast to previous variational inference-based solvers. Empirically, we evaluate the proposed solver with MNIST and Fashion-MNIST datasets. Our results show that under a comparable reconstruction quality, an adversary suffers from higher prediction error from clustering our compressed codes than that with the compared methods. Most importantly, our solver is independent to private information in inference phase contrary to the baselines.
|
[
"['Teng-Hui Huang' 'Hesham El Gamal']"
] |
null | null |
2403.04781
| null | null |
http://arxiv.org/pdf/2403.04781v1
|
2024-03-02T11:20:24Z
|
2024-03-02T11:20:24Z
|
Selective Encryption using Segmentation Mask with Chaotic Henon Map for
Multidimensional Medical Images
|
A user-centric design and resource optimization should be at the center of any technology or innovation. The user-centric perspective gives the developer the opportunity to develop with task-based optimization. The user in the medical image field is a medical professional who analyzes the medical images and gives their diagnosis results to the patient. This scheme, having the medical professional user's perspective, innovates in the area of Medical Image storage and security. The architecture is designed with three main segments, namely: Segmentation, Storage, and Retrieval. This architecture was designed owing to the fact that the number of retrieval operations done by medical professionals was toweringly higher when compared to the storage operations done for some handful number of times for a particular medical image. This gives room for our innovation to segment out the medically indispensable part of the medical image, encrypt it, and store it. By encrypting the vital parts of the image using a strong encryption algorithm like the chaotic Henon map, we are able to keep the security intact. Now retrieving the medical image demands only the computationally less stressing decryption of the segmented region of interest. The decryption of the segmented region of interest results in the full recovery of the medical image which can be viewed on demand by the medical professionals for various diagnosis purposes. In this scheme, we were able to achieve a retrieval speed improvement of around 47% when compared to a full image encryption of brain medical CT images.
|
[
"['S Arut Prakash' 'Aditya Ganesh Kumar' 'Prabhu Shankar K. C.'\n 'Lithicka Anandavel' 'Aditya Lakshmi Narayanan']"
] |
null | null |
2403.04783
| null | null |
http://arxiv.org/pdf/2403.04783v1
|
2024-03-02T16:52:22Z
|
2024-03-02T16:52:22Z
|
AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
|
Despite extensive pre-training and fine-tuning in moral alignment to prevent generating harmful information at user request, large language models (LLMs) remain vulnerable to jailbreak attacks. In this paper, we propose AutoDefense, a response-filtering based multi-agent defense framework that filters harmful responses from LLMs. This framework assigns different roles to LLM agents and employs them to complete the defense task collaboratively. The division in tasks enhances the overall instruction-following of LLMs and enables the integration of other defense components as tools. AutoDefense can adapt to various sizes and kinds of open-source LLMs that serve as agents. Through conducting extensive experiments on a large scale of harmful and safe prompts, we validate the effectiveness of the proposed AutoDefense in improving the robustness against jailbreak attacks, while maintaining the performance at normal user request. Our code and data are publicly available at https://github.com/XHMY/AutoDefense.
|
[
"['Yifan Zeng' 'Yiran Wu' 'Xiao Zhang' 'Huazheng Wang' 'Qingyun Wu']"
] |
null | null |
2403.04784
| null | null |
http://arxiv.org/pdf/2403.04784v1
|
2024-03-02T20:25:38Z
|
2024-03-02T20:25:38Z
|
Analysis of Privacy Leakage in Federated Large Language Models
|
With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need for significant modifications to FL to accommodate the large-scale of LLMs. While substantial adjustments to the protocol have been introduced as a response, comprehensive privacy analysis for the adapted FL protocol is currently lacking. To address this gap, our work delves into an extensive examination of the privacy analysis of FL when used for training LLMs, both from theoretical and practical perspectives. In particular, we design two active membership inference attacks with guaranteed theoretical success rates to assess the privacy leakages of various adapted FL configurations. Our theoretical findings are translated into practical attacks, revealing substantial privacy vulnerabilities in popular LLMs, including BERT, RoBERTa, DistilBERT, and OpenAI's GPTs, across multiple real-world language datasets. Additionally, we conduct thorough experiments to evaluate the privacy leakage of these models when data is protected by state-of-the-art differential privacy (DP) mechanisms.
|
[
"['Minh N. Vu' 'Truc Nguyen' \"Tre' R. Jeter\" 'My T. Thai']"
] |
null | null |
2403.04789
| null | null |
http://arxiv.org/pdf/2403.04789v2
|
2024-03-11T01:04:28Z
|
2024-03-04T08:38:53Z
|
TopicDiff: A Topic-enriched Diffusion Approach for Multimodal
Conversational Emotion Detection
|
Multimodal Conversational Emotion (MCE) detection, generally spanning across the acoustic, vision and language modalities, has attracted increasing interest in the multimedia community. Previous studies predominantly focus on learning contextual information in conversations with only a few considering the topic information in single language modality, while always neglecting the acoustic and vision topic information. On this basis, we propose a model-agnostic Topic-enriched Diffusion (TopicDiff) approach for capturing multimodal topic information in MCE tasks. Particularly, we integrate the diffusion model into neural topic model to alleviate the diversity deficiency problem of neural topic model in capturing topic information. Detailed evaluations demonstrate the significant improvements of TopicDiff over the state-of-the-art MCE baselines, justifying the importance of multimodal topic information to MCE and the effectiveness of TopicDiff in capturing such information. Furthermore, we observe an interesting finding that the topic information in acoustic and vision is more discriminative and robust compared to the language.
|
[
"['Jiamin Luo' 'Jingjing Wang' 'Guodong Zhou']"
] |
null | null |
2403.04791
| null | null |
http://arxiv.org/pdf/2403.04791v1
|
2024-03-04T10:13:30Z
|
2024-03-04T10:13:30Z
|
LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK
Case Law Dataset
|
To undertake computational research of the law, efficiently identifying datasets of court decisions that relate to a specific legal issue is a crucial yet challenging endeavour. This study addresses the gap in the literature working with large legal corpora about how to isolate cases, in our case summary judgments, from a large corpus of UK court decisions. We introduce a comparative analysis of two computational methods: (1) a traditional natural language processing-based approach leveraging expert-generated keywords and logical operators and (2) an innovative application of the Claude 2 large language model to classify cases based on content-specific prompts. We use the Cambridge Law Corpus of 356,011 UK court decisions and determine that the large language model achieves a weighted F1 score of 0.94 versus 0.78 for keywords. Despite iterative refinement, the search logic based on keywords fails to capture nuances in legal language. We identify and extract 3,102 summary judgment cases, enabling us to map their distribution across various UK courts over a temporal span. The paper marks a pioneering step in employing advanced natural language processing to tackle core legal research tasks, demonstrating how these technologies can bridge systemic gaps and enhance the accessibility of legal information. We share the extracted dataset metrics to support further research on summary judgments.
|
[
"['Ahmed Izzidien' 'Holli Sargeant' 'Felix Steffek']"
] |
null | null |
2403.04792
| null | null |
http://arxiv.org/pdf/2403.04792v1
|
2024-03-04T14:01:11Z
|
2024-03-04T14:01:11Z
|
Breaking the Language Barrier: Can Direct Inference Outperform
Pre-Translation in Multilingual LLM Applications?
|
Large language models hold significant promise in multilingual applications. However, inherent biases stemming from predominantly English-centric pre-training have led to the widespread practice of pre-translation, i.e., translating non-English inputs to English before inference, leading to complexity and information loss. This study re-evaluates the need for pre-translation in the context of PaLM2 models (Anil et al., 2023), which have been established as highly performant in multilingual tasks. We offer a comprehensive investigation across 108 languages and 6 diverse benchmarks, including open-end generative tasks, which were excluded from previous similar studies. Our findings challenge the pre-translation paradigm established in prior research, highlighting the advantages of direct inference in PaLM2. Specifically, PaLM2-L consistently outperforms pre-translation in 94 out of 108 languages. These findings pave the way for more efficient and effective multilingual applications, alleviating the limitations associated with pre-translation and unlocking linguistic authenticity.
|
[
"['Yotam Intrator' 'Matan Halfon' 'Roman Goldenberg' 'Reut Tsarfaty'\n 'Matan Eyal' 'Ehud Rivlin' 'Yossi Matias' 'Natalia Aizenberg']"
] |
null | null |
2403.04793
| null | null |
http://arxiv.org/abs/2403.04793v1
|
2024-03-04T14:20:41Z
|
2024-03-04T14:20:41Z
|
A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time
Series
|
Causal inference is a fundamental research topic for discovering the cause-effect relationships in many disciplines. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to identify linear relationships, while others are applicable for non-linearities. Algorithms further vary in their sensitivity to noise and their ability to infer causal information from coupled vs. non-coupled time series. Therefore, different algorithms often generate different causal relationships for the same input. To achieve a more robust causal inference result, this publication proposes a novel data-driven two-phase multi-split causal ensemble model to combine the strengths of different causality base algorithms. In comparison to existing approaches, the proposed ensemble method reduces the influence of noise through a data partitioning scheme in the first phase. To achieve this, the data are initially divided into several partitions and the base algorithms are applied to each partition. Subsequently, Gaussian mixture models are used to identify the causal relationships derived from the different partitions that are likely to be valid. In the second phase, the identified relationships from each base algorithm are then merged based on three combination rules. The proposed ensemble approach is evaluated using multiple metrics, among them a newly developed evaluation index for causal ensemble approaches. We perform experiments using three synthetic datasets with different volumes and complexity, which are specifically designed to test causality detection methods under different circumstances while knowing the ground truth causal relationships. In these experiments, our causality ensemble outperforms each of its base algorithms. In practical applications, the use of the proposed method could hence lead to more robust and reliable causality results.
|
[
"['Zhipeng Ma' 'Marco Kemmerling' 'Daniel Buschmann' 'Chrismarie Enslin'\n 'Daniel Lütticke' 'Robert H. Schmitt']"
] |
null | null |
2403.04795
| null | null |
http://arxiv.org/pdf/2403.04795v1
|
2024-03-04T16:18:36Z
|
2024-03-04T16:18:36Z
|
Large Language Models in Fire Engineering: An Examination of Technical
Questions Against Domain Knowledge
|
This communication presents preliminary findings from comparing two recent chatbots, OpenAI's ChatGPT and Google's Bard, in the context of fire engineering by evaluating their responses in handling fire safety related queries. A diverse range of fire engineering questions and scenarios were created and examined, including structural fire design, fire prevention strategies, evacuation, building code compliance, and fire suppression systems (some of which resemble those commonly present in the Fire Protection exam (FPE)). The results reveal some key differences in the performance of the chatbots, with ChatGPT demonstrating a relatively superior performance. Then, this communication highlights the potential for chatbot technology to revolutionize fire engineering practices by providing instant access to critical information while outlining areas for further improvement and research. Evidently, and when it matures, this technology will likely be elemental to our engineers' practice and education.
|
[
"['Haley Hostetter' 'M. Z. Naser' 'Xinyan Huang' 'John Gales']"
] |
null | null |
2403.04797
| null | null |
http://arxiv.org/pdf/2403.04797v1
|
2024-03-05T04:58:37Z
|
2024-03-05T04:58:37Z
|
Found in the Middle: How Language Models Use Long Contexts Better via
Plug-and-Play Positional Encoding
|
This paper aims to overcome the "lost-in-the-middle" challenge of large language models (LLMs). While recent advancements have successfully enabled LLMs to perform stable language modeling with up to 4 million tokens, the persistent difficulty faced by most LLMs in identifying relevant information situated in the middle of the context has not been adequately tackled. To address this problem, this paper introduces Multi-scale Positional Encoding (Ms-PoE) which is a simple yet effective plug-and-play approach to enhance the capacity of LLMs to handle the relevant information located in the middle of the context, without fine-tuning or introducing any additional overhead. Ms-PoE leverages the position indice rescaling to relieve the long-term decay effect introduced by RoPE, while meticulously assigning distinct scaling ratios to different attention heads to preserve essential knowledge learned during the pre-training step, forming a multi-scale context fusion from short to long distance. Extensive experiments with a wide range of LLMs demonstrate the efficacy of our approach. Notably, Ms-PoE achieves an average accuracy gain of up to 3.8 on the Zero-SCROLLS benchmark over the original LLMs. Code are available at https://github.com/VITA-Group/Ms-PoE.
|
[
"['Zhenyu Zhang' 'Runjin Chen' 'Shiwei Liu' 'Zhewei Yao' 'Olatunji Ruwase'\n 'Beidi Chen' 'Xiaoxia Wu' 'Zhangyang Wang']"
] |
null | null |
2403.04798
| null | null |
http://arxiv.org/pdf/2403.04798v2
|
2024-04-02T14:52:37Z
|
2024-03-05T12:07:18Z
|
JMI at SemEval 2024 Task 3: Two-step approach for multimodal ECAC using
in-context learning with GPT and instruction-tuned Llama models
|
This paper presents our system development for SemEval-2024 Task 3: "The Competition of Multimodal Emotion Cause Analysis in Conversations". Effectively capturing emotions in human conversations requires integrating multiple modalities such as text, audio, and video. However, the complexities of these diverse modalities pose challenges for developing an efficient multimodal emotion cause analysis (ECA) system. Our proposed approach addresses these challenges by a two-step framework. We adopt two different approaches in our implementation. In Approach 1, we employ instruction-tuning with two separate Llama 2 models for emotion and cause prediction. In Approach 2, we use GPT-4V for conversation-level video description and employ in-context learning with annotated conversation using GPT 3.5. Our system wins rank 4, and system ablation experiments demonstrate that our proposed solutions achieve significant performance gains. All the experimental codes are available on Github.
|
[
"['Arefa' 'Mohammed Abbas Ansari' 'Chandni Saxena' 'Tanvir Ahmad']"
] |
null | null |
2403.04800
| null | null |
http://arxiv.org/pdf/2403.04800v1
|
2024-03-05T18:52:50Z
|
2024-03-05T18:52:50Z
|
(Un)paired signal-to-signal translation with 1D conditional GANs
|
I show that a one-dimensional (1D) conditional generative adversarial network (cGAN) with an adversarial training architecture is capable of unpaired signal-to-signal ("sig2sig") translation. Using a simplified CycleGAN model with 1D layers and wider convolutional kernels, mirroring WaveGAN to reframe two-dimensional (2D) image generation as 1D audio generation, I show that recasting the 2D image-to-image translation task to a 1D signal-to-signal translation task with deep convolutional GANs is possible without substantial modification to the conventional U-Net model and adversarial architecture developed as CycleGAN. With this I show for a small tunable dataset that noisy test signals unseen by the 1D CycleGAN model and without paired training transform from the source domain to signals similar to paired test signals in the translated domain, especially in terms of frequency, and I quantify these differences in terms of correlation and error.
|
[
"['Eric Easthope']"
] |
null | null |
2403.04803
| null | null |
http://arxiv.org/pdf/2403.04803v1
|
2024-03-05T20:54:56Z
|
2024-03-05T20:54:56Z
|
Enhancing Security in Federated Learning through Adaptive
Consensus-Based Model Update Validation
|
This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a simplified consensus-based verification process integrated with an adaptive thresholding mechanism. This dynamic thresholding is designed to adjust based on the evolving landscape of model updates, offering a refined layer of anomaly detection that aligns with the real-time needs of distributed learning environments. Our method necessitates a majority consensus among participating clients to validate updates, ensuring that only vetted and consensual modifications are applied to the global model. The efficacy of our approach is validated through experiments on two benchmark datasets in deep learning, CIFAR-10 and MNIST. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience. This method transcends conventional techniques that depend on anomaly detection or statistical validation by incorporating a verification layer reminiscent of blockchain's participatory validation without the associated cryptographic overhead. The innovation of our approach rests in striking an optimal balance between heightened security measures and the inherent limitations of FL systems, such as computational efficiency and data privacy. Implementing a consensus mechanism specifically tailored for FL environments paves the way for more secure, robust, and trustworthy distributed machine learning applications, where safeguarding data integrity and model robustness is critical.
|
[
"['Zahir Alsulaimawi']"
] |
null | null |
2403.04804
| null | null |
http://arxiv.org/pdf/2403.04804v1
|
2024-03-05T22:09:58Z
|
2024-03-05T22:09:58Z
|
AttentionStitch: How Attention Solves the Speech Editing Problem
|
The generation of natural and high-quality speech from text is a challenging problem in the field of natural language processing. In addition to speech generation, speech editing is also a crucial task, which requires the seamless and unnoticeable integration of edited speech into synthesized speech. We propose a novel approach to speech editing by leveraging a pre-trained text-to-speech (TTS) model, such as FastSpeech 2, and incorporating a double attention block network on top of it to automatically merge the synthesized mel-spectrogram with the mel-spectrogram of the edited text. We refer to this model as AttentionStitch, as it harnesses attention to stitch audio samples together. We evaluate the proposed AttentionStitch model against state-of-the-art baselines on both single and multi-speaker datasets, namely LJSpeech and VCTK. We demonstrate its superior performance through an objective and a subjective evaluation test involving 15 human participants. AttentionStitch is capable of producing high-quality speech, even for words not seen during training, while operating automatically without the need for human intervention. Moreover, AttentionStitch is fast during both training and inference and is able to generate human-sounding edited speech.
|
[
"['Antonios Alexos' 'Pierre Baldi']"
] |
null | null |
2403.04805
| null | null |
http://arxiv.org/pdf/2403.04805v1
|
2024-03-05T23:02:55Z
|
2024-03-05T23:02:55Z
|
Not all tickets are equal and we know it: Guiding pruning with
domain-specific knowledge
|
Neural structure learning is of paramount importance for scientific discovery and interpretability. Yet, contemporary pruning algorithms that focus on computational resource efficiency face algorithmic barriers to select a meaningful model that aligns with domain expertise. To mitigate this challenge, we propose DASH, which guides pruning by available domain-specific structural information. In the context of learning dynamic gene regulatory network models, we show that DASH combined with existing general knowledge on interaction partners provides data-specific insights aligned with biology. For this task, we show on synthetic data with ground truth information and two real world applications the effectiveness of DASH, which outperforms competing methods by a large margin and provides more meaningful biological insights. Our work shows that domain specific structural information bears the potential to improve model-derived scientific insights.
|
[
"['Intekhab Hossain' 'Jonas Fischer' 'Rebekka Burkholz' 'John Quackenbush']"
] |
null | null |
2403.04807
| null | null |
http://arxiv.org/pdf/2403.04807v1
|
2024-03-06T08:45:29Z
|
2024-03-06T08:45:29Z
|
Mathematics of Neural Networks (Lecture Notes Graduate Course)
|
These are the lecture notes that accompanied the course of the same name that I taught at the Eindhoven University of Technology from 2021 to 2023. The course is intended as an introduction to neural networks for mathematics students at the graduate level and aims to make mathematics students interested in further researching neural networks. It consists of two parts: first a general introduction to deep learning that focuses on introducing the field in a formal mathematical way. The second part provides an introduction to the theory of Lie groups and homogeneous spaces and how it can be applied to design neural networks with desirable geometric equivariances. The lecture notes were made to be as self-contained as possible so as to accessible for any student with a moderate mathematics background. The course also included coding tutorials and assignments in the form of a set of Jupyter notebooks that are publicly available at https://gitlab.com/bsmetsjr/mathematics_of_neural_networks.
|
[
"['Bart M. N. Smets']"
] |
null | null |
2403.04808
| null | null |
http://arxiv.org/pdf/2403.04808v1
|
2024-03-06T10:55:30Z
|
2024-03-06T10:55:30Z
|
WaterMax: breaking the LLM watermark detectability-robustness-quality
trade-off
|
Watermarking is a technical means to dissuade malfeasant usage of Large Language Models. This paper proposes a novel watermarking scheme, so-called WaterMax, that enjoys high detectability while sustaining the quality of the generated text of the original LLM. Its new design leaves the LLM untouched (no modification of the weights, logits, temperature, or sampling technique). WaterMax balances robustness and complexity contrary to the watermarking techniques of the literature inherently provoking a trade-off between quality and robustness. Its performance is both theoretically proven and experimentally validated. It outperforms all the SotA techniques under the most complete benchmark suite.
|
[
"['Eva Giboulot' 'Furon Teddy']"
] |
null | null |
2403.04809
| null | null |
http://arxiv.org/pdf/2403.04809v1
|
2024-03-06T18:33:27Z
|
2024-03-06T18:33:27Z
|
Investigation of the Impact of Synthetic Training Data in the Industrial
Application of Terminal Strip Object Detection
|
In industrial manufacturing, numerous tasks of visually inspecting or detecting specific objects exist that are currently performed manually or by classical image processing methods. Therefore, introducing recent deep learning models to industrial environments holds the potential to increase productivity and enable new applications. However, gathering and labeling sufficient data is often intractable, complicating the implementation of such projects. Hence, image synthesis methods are commonly used to generate synthetic training data from 3D models and annotate them automatically, although it results in a sim-to-real domain gap. In this paper, we investigate the sim-to-real generalization performance of standard object detectors on the complex industrial application of terminal strip object detection. Combining domain randomization and domain knowledge, we created an image synthesis pipeline for automatically generating the training data. Moreover, we manually annotated 300 real images of terminal strips for the evaluation. The results show the cruciality of the objects of interest to have the same scale in either domain. Nevertheless, under optimized scaling conditions, the sim-to-real performance difference in mean average precision amounts to 2.69 % for RetinaNet and 0.98 % for Faster R-CNN, qualifying this approach for industrial requirements.
|
[
"['Nico Baumgart' 'Markus Lange-Hegermann' 'Mike Mücke']"
] |
null | null |
2403.04810
| null | null |
http://arxiv.org/pdf/2403.04810v3
|
2024-04-08T11:51:31Z
|
2024-03-06T19:09:11Z
|
Restricted Bayesian Neural Network
|
Modern deep learning tools are remarkably effective in addressing intricate problems. However, their operation as black-box models introduces increased uncertainty in predictions. Additionally, they contend with various challenges, including the need for substantial storage space in large networks, issues of overfitting, underfitting, vanishing gradients, and more. This study explores the concept of Bayesian Neural Networks, presenting a novel architecture designed to significantly alleviate the storage space complexity of a network. Furthermore, we introduce an algorithm adept at efficiently handling uncertainties, ensuring robust convergence values without becoming trapped in local optima, particularly when the objective function lacks perfect convexity.
|
[
"['Sourav Ganguly' 'Saprativa Bhattacharjee']"
] |
null | null |
2403.04811
| null | null |
http://arxiv.org/pdf/2403.04811v1
|
2024-03-06T21:45:35Z
|
2024-03-06T21:45:35Z
|
Quantifying Contamination in Evaluating Code Generation Capabilities of
Language Models
|
While large language models have achieved remarkable performance on various code generation benchmarks, there have been growing concerns regarding potential contamination of these benchmarks as they may be leaked into pretraining and finetuning data. While recent work has investigated contamination in natural language generation and understanding tasks, there has been less extensive research into how data contamination impacts the evaluation of code generation, which is critical for understanding the robustness and reliability of LLMs in programming contexts. In this work, we perform a comprehensive study of data contamination of popular code generation benchmarks, and precisely quantify their overlap with pretraining corpus through both surface-level and semantic-level matching. In our experiments, we show that there are substantial overlap between popular code generation benchmarks and open training corpus, and models perform significantly better on the subset of the benchmarks where similar solutions are seen during training. We also conduct extensive analysis on the factors that affects model memorization and generalization, such as model size, problem difficulty, and question length. We release all resulting files from our matching pipeline for future research.
|
[
"['Martin Riddell' 'Ansong Ni' 'Arman Cohan']"
] |
null | null |
2403.04812
| null | null |
http://arxiv.org/pdf/2403.04812v1
|
2024-03-07T01:00:55Z
|
2024-03-07T01:00:55Z
|
TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
|
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows. Such predictions are beneficial for understanding the situation and making decisions in traffic control. However, most state-of-the-art DL models are considered "black boxes" with little to no transparency for end users with respect to the underlying mechanisms. Some previous work tried to "open the black boxes" and increase the interpretability of how predictions are generated. However, it still remains challenging to handle complex models on large-scale spatio-temporal data and discover salient spatial and temporal patterns that significantly influence traffic flows. To overcome the challenges, we present TrafPS, a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning. The measurements, region SHAP and trajectory SHAP, are proposed to quantify the impact of flow patterns on urban traffic at different levels. Based on the task requirement from the domain experts, we employ an interactive visual interface for multi-aspect exploration and analysis of significant flow patterns. Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and decision-making support for urban planning.
|
[
"['Zezheng Feng' 'Yifan Jiang' 'Hongjun Wang' 'Zipei Fan' 'Yuxin Ma'\n 'Shuang-Hua Yang' 'Huamin Qu' 'Xuan Song']"
] |
null | null |
2403.04814
| null | null |
http://arxiv.org/pdf/2403.04814v3
|
2024-06-23T01:17:48Z
|
2024-03-07T05:05:56Z
|
Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks
|
We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for evaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM) task. This benchmark focuses on syntax-aware completions of program structures such as code blocks and conditional expressions, and includes 17,720 examples from multiple programming languages, sourced from recent code submissions after April 2022 to minimize data contamination. SAFIM provides a robust framework with various prompt designs and novel syntax-aware post-processing techniques, facilitating accurate and fair comparisons across LLMs. Our comprehensive evaluation of 15 LLMs shows that FIM pretraining not only enhances FIM proficiency but also improves Left-to-Right (L2R) inference using LLMs. Our findings challenge conventional beliefs and suggest that pretraining methods and data quality have more impact than model size. SAFIM thus serves as a foundational platform for future research in effective pretraining strategies for code LLMs. The evaluation toolkit and dataset are available at https://github.com/gonglinyuan/safim, and the leaderboard is available at https://safimbenchmark.com.
|
[
"['Linyuan Gong' 'Sida Wang' 'Mostafa Elhoushi' 'Alvin Cheung']"
] |
null | null |
2403.04818
| null | null |
http://arxiv.org/pdf/2403.04818v1
|
2024-03-07T13:19:38Z
|
2024-03-07T13:19:38Z
|
Storm Surge Modeling in the AI ERA: Using LSTM-based Machine Learning
for Enhancing Forecasting Accuracy
|
Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of an LSTM-based deep learning network machine learning (ML) architecture for capturing and predicting the behavior of the systemic error for storm surge forecast models with respect to real-world water height observations from gauge stations during hurricane events. The overall goal of this work is to predict the systemic error of the physics model and use it to improve the accuracy of the simulation results post factum. We trained our proposed ML model on a dataset of 61 historical storms in the coastal regions of the U.S. and we tested its performance in bias correcting modeled water level data predictions from hurricane Ian (2022). We show that our model can consistently improve the forecasting accuracy for hurricane Ian -- unknown to the ML model -- at all gauge station coordinates used for the initial data. Moreover, by examining the impact of using different subsets of the initial training dataset, containing a number of relatively similar or different hurricanes in terms of hurricane track, we found that we can obtain similar quality of bias correction by only using a subset of six hurricanes. This is an important result that implies the possibility to apply a pre-trained ML model to real-time hurricane forecasting results with the goal of bias correcting and improving the produced simulation accuracy. The presented work is an important first step in creating a bias correction system for real-time storm surge forecasting applicable to the full simulation area. It also presents a highly transferable and operationally applicable methodology for improving the accuracy in a wide range of physics simulation scenarios beyond storm surge forecasting.
|
[
"['Stefanos Giaremis' 'Noujoud Nader' 'Clint Dawson' 'Hartmut Kaiser'\n 'Carola Kaiser' 'Efstratios Nikidis']"
] |
null | null |
2403.04822
| null | null |
http://arxiv.org/pdf/2403.04822v2
|
2024-05-27T15:39:51Z
|
2024-03-07T15:44:50Z
|
UniTable: Towards a Unified Framework for Table Recognition via
Self-Supervised Pretraining
|
Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse. Prior work on table recognition (TR) has mainly centered around complex task-specific combinations of available inputs and tools. We present UniTable, a training framework that unifies both the training paradigm and training objective of TR. Its training paradigm combines the simplicity of purely pixel-level inputs with the effectiveness and scalability empowered by self-supervised pretraining from diverse unannotated tabular images. Our framework unifies the training objectives of all three TR tasks - extracting table structure, cell content, and cell bounding box - into a unified task-agnostic training objective: language modeling. Extensive quantitative and qualitative analyses highlight UniTable's state-of-the-art (SOTA) performance on four of the largest TR datasets. UniTable's table parsing capability has surpassed both existing TR methods and general large vision-language models, e.g., GPT-4o, GPT-4-turbo with vision, and LLaVA. Our code is publicly available at https://github.com/poloclub/unitable, featuring a Jupyter Notebook that includes the complete inference pipeline, fine-tuned across multiple TR datasets, supporting all three TR tasks.
|
[
"['ShengYun Peng' 'Aishwarya Chakravarthy' 'Seongmin Lee' 'Xiaojing Wang'\n 'Rajarajeswari Balasubramaniyan' 'Duen Horng Chau']"
] |
null | null |
2403.04837
| null | null |
http://arxiv.org/abs/2403.04837v1
|
2024-03-07T19:00:02Z
|
2024-03-07T19:00:02Z
|
Cell reprogramming design by transfer learning of functional
transcriptional networks
|
Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cells. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates, thereby generating a model of the network dynamics that can be transferred to specific reprogramming goals. The approach combines transcriptional responses to gene perturbations to minimize the difference between a given pair of initial and target transcriptional states. We demonstrate our approach's versatility by applying it to a microarray dataset comprising >9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases with decreasing developmental relatedness and that fewer genes are needed to progress along developmental paths than to regress. These findings establish a proof-of-concept for our approach to computationally design control strategies and provide insights into how gene regulatory networks govern phenotype.
|
[
"['Thomas P. Wytock' 'Adilson E. Motter']"
] |
null | null |
2403.04847
| null | null |
http://arxiv.org/pdf/2403.04847v2
|
2024-06-10T11:43:17Z
|
2024-03-07T19:02:13Z
|
Solving Inverse Problems with Model Mismatch using Untrained Neural
Networks within Model-based Architectures
|
Model-based deep learning methods such as loop unrolling (LU) and deep equilibrium model}(DEQ) extensions offer outstanding performance in solving inverse problems (IP). These methods unroll the optimization iterations into a sequence of neural networks that in effect learn a regularization function from data. While these architectures are currently state-of-the-art in numerous applications, their success heavily relies on the accuracy of the forward model. This assumption can be limiting in many physical applications due to model simplifications or uncertainties in the apparatus. To address forward model mismatch, we introduce an untrained forward model residual block within the model-based architecture to match the data consistency in the measurement domain for each instance. We propose two variants in well-known model-based architectures (LU and DEQ) and prove convergence under mild conditions. Our approach offers a unified solution that is less parameter-sensitive, requires no additional data, and enables simultaneous fitting of the forward model and reconstruction in a single pass, benefiting both linear and nonlinear inverse problems. The experiments show significant quality improvement in removing artifacts and preserving details across three distinct applications, encompassing both linear and nonlinear inverse problems. Moreover, we highlight reconstruction effectiveness in intermediate steps and showcase robustness to random initialization of the residual block and a higher number of iterations during evaluation. Code is available at texttt{https://github.com/InvProbs/A-adaptive-model-based-methods}.
|
[
"['Peimeng Guan' 'Naveed Iqbal' 'Mark A. Davenport' 'Mudassir Masood']"
] |
null | null |
2403.04861
| null | null |
http://arxiv.org/pdf/2403.04861v2
|
2024-03-12T18:35:48Z
|
2024-03-07T19:27:01Z
|
A Survey of Lottery Ticket Hypothesis
|
The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a highly sparse subnetwork (i.e., winning tickets) that can achieve even better performance than the original model when trained in isolation. While LTH has been proved both empirically and theoretically in many works, there still are some open issues, such as efficiency and scalability, to be addressed. Also, the lack of open-source frameworks and consensual experimental setting poses a challenge to future research on LTH. We, for the first time, examine previous research and studies on LTH from different perspectives. We also discuss issues in existing works and list potential directions for further exploration. This survey aims to provide an in-depth look at the state of LTH and develop a duly maintained platform to conduct experiments and compare with the most updated baselines.
|
[
"['Bohan Liu' 'Zijie Zhang' 'Peixiong He' 'Zhensen Wang' 'Yang Xiao'\n 'Ruimeng Ye' 'Yang Zhou' 'Wei-Shinn Ku' 'Bo Hui']"
] |
null | null |
2403.04867
| null | null |
http://arxiv.org/pdf/2403.04867v2
|
2024-06-10T19:44:02Z
|
2024-03-07T19:36:05Z
|
Unified Mechanism-Specific Amplification by Subsampling and Group
Privacy Amplification
|
Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with R'enyi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results.
|
[
"['Jan Schuchardt' 'Mihail Stoian' 'Arthur Kosmala' 'Stephan Günnemann']"
] |
null | null |
2403.04875
| null | null |
http://arxiv.org/pdf/2403.04875v1
|
2024-03-07T19:47:48Z
|
2024-03-07T19:47:48Z
|
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning
|
Adaptations of Transformer models, such as BERT4Rec and SASRec, achieve state-of-the-art performance in the sequential recommendation task according to accuracy-based metrics, such as NDCG. These models treat items as tokens and then utilise a score-and-rank approach (Top-K strategy), where the model first computes item scores and then ranks them according to this score. While this approach works well for accuracy-based metrics, it is hard to use it for optimising more complex beyond-accuracy metrics such as diversity. Recently, the GPTRec model, which uses a different Next-K strategy, has been proposed as an alternative to the Top-K models. In contrast with traditional Top-K recommendations, Next-K generates recommendations item-by-item and, therefore, can account for complex item-to-item interdependencies important for the beyond-accuracy measures. However, the original GPTRec paper focused only on accuracy in experiments and needed to address how to optimise the model for complex beyond-accuracy metrics. Indeed, training GPTRec for beyond-accuracy goals is challenging because the interaction training data available for training recommender systems typically needs to be aligned with beyond-accuracy recommendation goals. To solve the misalignment problem, we train GPTRec using a 2-stage approach: in the first stage, we use a teacher-student approach to train GPTRec, mimicking the behaviour of traditional Top-K models; in the second stage, we use Reinforcement Learning to align the model for beyond-accuracy goals. In particular, we experiment with increasing recommendation diversity and reducing popularity bias. Our experiments on two datasets show that in 3 out of 4 cases, GPTRec's Next-K generation approach offers a better tradeoff between accuracy and secondary metrics than classic greedy re-ranking techniques.
|
[
"['Aleksandr Petrov' 'Craig Macdonald']"
] |
null | null |
2403.04882
| null | null |
http://arxiv.org/pdf/2403.04882v1
|
2024-03-07T20:14:20Z
|
2024-03-07T20:14:20Z
|
Efficient High-Resolution Time Series Classification via Attention
Kronecker Decomposition
|
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effectively, it is imperative that the state-of-the-art attention model is scalable to accommodate the growing sequence lengths typically encountered in high-resolution time series data, while also demonstrating robustness in handling the inherent noise prevalent in such datasets. To address this, we propose to hierarchically encode the long time series into multiple levels based on the interaction ranges. By capturing relationships at different levels, we can build more robust, expressive, and efficient models that are capable of capturing both short-term fluctuations and long-term trends in the data. We then propose a new time series transformer backbone (KronTime) by introducing Kronecker-decomposed attention to process such multi-level time series, which sequentially calculates attention from the lower level to the upper level. Experiments on four long time series datasets demonstrate superior classification results with improved efficiency compared to baseline methods.
|
[
"['Aosong Feng' 'Jialin Chen' 'Juan Garza' 'Brooklyn Berry'\n 'Francisco Salazar' 'Yifeng Gao' 'Rex Ying' 'Leandros Tassiulas']"
] |
null | null |
2403.04883
| null | null |
http://arxiv.org/pdf/2403.04883v1
|
2024-03-07T20:16:18Z
|
2024-03-07T20:16:18Z
|
Learning Traveling Solitary Waves Using Separable Gaussian Neural
Networks
|
In this paper, we apply a machine-learning approach to learn traveling solitary waves across various families of partial differential equations (PDEs). Our approach integrates a novel interpretable neural network (NN) architecture, called Separable Gaussian Neural Networks (SGNN) into the framework of Physics-Informed Neural Networks (PINNs). Unlike the traditional PINNs that treat spatial and temporal data as independent inputs, the present method leverages wave characteristics to transform data into the so-called co-traveling wave frame. This adaptation effectively addresses the issue of propagation failure in PINNs when applied to large computational domains. Here, the SGNN architecture demonstrates robust approximation capabilities for single-peakon, multi-peakon, and stationary solutions within the (1+1)-dimensional, $b$-family of PDEs. In addition, we expand our investigations, and explore not only peakon solutions in the $ab$-family but also compacton solutions in (2+1)-dimensional, Rosenau-Hyman family of PDEs. A comparative analysis with MLP reveals that SGNN achieves comparable accuracy with fewer than a tenth of the neurons, underscoring its efficiency and potential for broader application in solving complex nonlinear PDEs.
|
[
"['Siyuan Xing' 'Efstathios G. Charalampidis']"
] |
null | null |
2403.04884
| null | null |
http://arxiv.org/pdf/2403.04884v2
|
2024-07-15T12:49:16Z
|
2024-03-07T20:16:42Z
|
Optimizing Retinal Prosthetic Stimuli with Conditional Invertible Neural
Networks
|
Implantable retinal prostheses offer a promising solution to restore partial vision by circumventing damaged photoreceptor cells in the retina and directly stimulating the remaining functional retinal cells. However, the information transmission between the camera and retinal cells is often limited by the low resolution of the electrode array and the lack of specificity for different ganglion cell types, resulting in suboptimal stimulations. In this work, we propose to utilize normalizing flow-based conditional invertible neural networks to optimize retinal implant stimulation in an unsupervised manner. The invertibility of these networks allows us to use them as a surrogate for the computational model of the visual system, while also encoding input camera signals into optimized electrical stimuli on the electrode array. Compared to other methods, such as trivial downsampling, linear models, and feed-forward convolutional neural networks, the flow-based invertible neural network and its conditional extension yield better visual reconstruction qualities w.r.t. various metrics using a physiologically validated simulation tool.
|
[
"['Yuli Wu' 'Julian Wittmann' 'Peter Walter' 'Johannes Stegmaier']"
] |
null | null |
2403.04919
| null | null |
http://arxiv.org/pdf/2403.04919v2
|
2024-05-22T21:43:39Z
|
2024-03-07T22:04:35Z
|
Identifying Causal Effects Under Functional Dependencies
|
We study the identification of causal effects, motivated by two improvements to identifiability which can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Second, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure which removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects.
|
[
"['Yizuo Chen' 'Adnan Darwiche']"
] |
null | null |
2403.04923
| null | null |
http://arxiv.org/pdf/2403.04923v2
|
2024-04-18T00:10:49Z
|
2024-03-07T22:14:04Z
|
Control-based Graph Embeddings with Data Augmentation for Contrastive
Learning
|
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introduces a novel framework for contrastive learning, a widely prevalent technique for unsupervised representation learning. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Though different from the original graphs, these augmented graphs retain the original graph's structural characteristics. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks. The core concept revolves around perturbing the original graph to create a new one while preserving the controllability properties specific to networks and graphs. Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks. The key innovation lies in our ability to decode the network structure using these control properties, opening new avenues for unsupervised graph representation learning.
|
[
"['Obaid Ullah Ahmad' 'Anwar Said' 'Mudassir Shabbir' 'Waseem Abbas'\n 'Xenofon Koutsoukos']"
] |
null | null |
2403.04929
| null | null |
http://arxiv.org/pdf/2403.04929v1
|
2024-03-07T22:35:22Z
|
2024-03-07T22:35:22Z
|
On the Markov Property of Neural Algorithmic Reasoning: Analyses and
Methods
|
Neural algorithmic reasoning is an emerging research direction that endows neural networks with the ability to mimic algorithmic executions step-by-step. A common paradigm in existing designs involves the use of historical embeddings in predicting the results of future execution steps. Our observation in this work is that such historical dependence intrinsically contradicts the Markov nature of algorithmic reasoning tasks. Based on this motivation, we present our ForgetNet, which does not use historical embeddings and thus is consistent with the Markov nature of the tasks. To address challenges in training ForgetNet at early stages, we further introduce G-ForgetNet, which uses a gating mechanism to allow for the selective integration of historical embeddings. Such an enhanced capability provides valuable computational pathways during the model's early training phase. Our extensive experiments, based on the CLRS-30 algorithmic reasoning benchmark, demonstrate that both ForgetNet and G-ForgetNet achieve better generalization capability than existing methods. Furthermore, we investigate the behavior of the gating mechanism, highlighting its degree of alignment with our intuitions and its effectiveness for robust performance.
|
[
"['Montgomery Bohde' 'Meng Liu' 'Alexandra Saxton' 'Shuiwang Ji']"
] |
null | null |
2403.04937
| null | null |
http://arxiv.org/pdf/2403.04937v1
|
2024-03-07T23:00:49Z
|
2024-03-07T23:00:49Z
|
Gradient-free neural topology optimization
|
Gradient-free optimizers allow for tackling problems regardless of the smoothness or differentiability of their objective function, but they require many more iterations to converge when compared to gradient-based algorithms. This has made them unviable for topology optimization due to the high computational cost per iteration and high dimensionality of these problems. We propose a pre-trained neural reparameterization strategy that leads to at least one order of magnitude decrease in iteration count when optimizing the designs in latent space, as opposed to the conventional approach without latent reparameterization. We demonstrate this via extensive computational experiments in- and out-of-distribution with the training data. Although gradient-based topology optimization is still more efficient for differentiable problems, such as compliance optimization of structures, we believe this work will open up a new path for problems where gradient information is not readily available (e.g. fracture).
|
[
"['Gawel Kus' 'Miguel A. Bessa']"
] |
null | null |
2403.04940
| null | null |
http://arxiv.org/pdf/2403.04940v1
|
2024-03-07T23:07:46Z
|
2024-03-07T23:07:46Z
|
A spatiotemporal style transfer algorithm for dynamic visual stimulus
generation
|
Understanding how visual information is encoded in biological and artificial systems often requires vision scientists to generate appropriate stimuli to test specific hypotheses. Although deep neural network models have revolutionized the field of image generation with methods such as image style transfer, available methods for video generation are scarce. Here, we introduce the Spatiotemporal Style Transfer (STST) algorithm, a dynamic visual stimulus generation framework that allows powerful manipulation and synthesis of video stimuli for vision research. It is based on a two-stream deep neural network model that factorizes spatial and temporal features to generate dynamic visual stimuli whose model layer activations are matched to those of input videos. As an example, we show that our algorithm enables the generation of model metamers, dynamic stimuli whose layer activations within our two-stream model are matched to those of natural videos. We show that these generated stimuli match the low-level spatiotemporal features of their natural counterparts but lack their high-level semantic features, making it a powerful paradigm to study object recognition. Late layer activations in deep vision models exhibited a lower similarity between natural and metameric stimuli compared to early layers, confirming the lack of high-level information in the generated stimuli. Finally, we use our generated stimuli to probe the representational capabilities of predictive coding deep networks. These results showcase potential applications of our algorithm as a versatile tool for dynamic stimulus generation in vision science.
|
[
"['Antonino Greco' 'Markus Siegel']"
] |
null | null |
2403.04945
| null | null |
http://arxiv.org/pdf/2403.04945v3
|
2024-06-18T07:15:09Z
|
2024-03-07T23:20:56Z
|
MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language
Models for Report Generation
|
Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise. To automate ECG report generation and ensure its versatility, we propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions. To facilitate future research, we establish a benchmark to evaluate MEIT with various LLMs backbones across two large-scale ECG datasets. Our approach uniquely aligns the representations of the ECG signal and the report, and we conduct extensive experiments to benchmark MEIT with nine open-source LLMs using more than 800,000 ECG reports. MEIT's results underscore the superior performance of instruction-tuned LLMs, showcasing their proficiency in quality report generation, zero-shot capabilities, and resilience to signal perturbation. These findings emphasize the efficacy of our MEIT framework and its potential for real-world clinical application.
|
[
"['Zhongwei Wan' 'Che Liu' 'Xin Wang' 'Chaofan Tao' 'Hui Shen'\n 'Zhenwu Peng' 'Jie Fu' 'Rossella Arcucci' 'Huaxiu Yao' 'Mi Zhang']"
] |
null | null |
2403.04960
| null | null |
http://arxiv.org/pdf/2403.04960v1
|
2024-03-08T00:02:30Z
|
2024-03-08T00:02:30Z
|
SecGPT: An Execution Isolation Architecture for LLM-Based Systems
|
Large language models (LLMs) extended as systems, such as ChatGPT, have begun supporting third-party applications. These LLM apps leverage the de facto natural language-based automated execution paradigm of LLMs: that is, apps and their interactions are defined in natural language, provided access to user data, and allowed to freely interact with each other and the system. These LLM app ecosystems resemble the settings of earlier computing platforms, where there was insufficient isolation between apps and the system. Because third-party apps may not be trustworthy, and exacerbated by the imprecision of the natural language interfaces, the current designs pose security and privacy risks for users. In this paper, we propose SecGPT, an architecture for LLM-based systems that aims to mitigate the security and privacy issues that arise with the execution of third-party apps. SecGPT's key idea is to isolate the execution of apps and more precisely mediate their interactions outside of their isolated environments. We evaluate SecGPT against a number of case study attacks and demonstrate that it protects against many security, privacy, and safety issues that exist in non-isolated LLM-based systems. The performance overhead incurred by SecGPT to improve security is under 0.3x for three-quarters of the tested queries. To foster follow-up research, we release SecGPT's source code at https://github.com/llm-platform-security/SecGPT.
|
[
"['Yuhao Wu' 'Franziska Roesner' 'Tadayoshi Kohno' 'Ning Zhang'\n 'Umar Iqbal']"
] |
null | null |
2403.04962
| null | null |
http://arxiv.org/pdf/2403.04962v2
|
2024-05-13T07:09:55Z
|
2024-03-08T00:15:43Z
|
C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer
Grading
|
Graph-based learning approaches, due to their ability to encode tissue/organ structure information, are increasingly favored for grading colorectal cancer histology images. Recent graph-based techniques involve dividing whole slide images (WSIs) into smaller or medium-sized patches, and then building graphs on each patch for direct use in training. This method, however, fails to capture the tissue structure information present in an entire WSI and relies on training from a significantly large dataset of image patches. In this paper, we propose a novel cell-to-patch graph convolutional network (C2P-GCN), which is a two-stage graph formation-based approach. In the first stage, it forms a patch-level graph based on the cell organization on each patch of a WSI. In the second stage, it forms an image-level graph based on a similarity measure between patches of a WSI considering each patch as a node of a graph. This graph representation is then fed into a multi-layer GCN-based classification network. Our approach, through its dual-phase graph construction, effectively gathers local structural details from individual patches and establishes a meaningful connection among all patches across a WSI. As C2P-GCN integrates the structural data of an entire WSI into a single graph, it allows our model to work with significantly fewer training data compared to the latest models for colorectal cancer. Experimental validation of C2P-GCN on two distinct colorectal cancer datasets demonstrates the effectiveness of our method.
|
[
"['Sudipta Paul' 'Bulent Yener' 'Amanda W. Lund']"
] |
null | null |
2403.04978
| null | null |
http://arxiv.org/pdf/2403.04978v1
|
2024-03-08T01:23:25Z
|
2024-03-08T01:23:25Z
|
Stacking as Accelerated Gradient Descent
|
Stacking, a heuristic technique for training deep residual networks by progressively increasing the number of layers and initializing new layers by copying parameters from older layers, has proven quite successful in improving the efficiency of training deep neural networks. In this paper, we propose a theoretical explanation for the efficacy of stacking: viz., stacking implements a form of Nesterov's accelerated gradient descent. The theory also covers simpler models such as the additive ensembles constructed in boosting methods, and provides an explanation for a similar widely-used practical heuristic for initializing the new classifier in each round of boosting. We also prove that for certain deep linear residual networks, stacking does provide accelerated training, via a new potential function analysis of the Nesterov's accelerated gradient method which allows errors in updates. We conduct proof-of-concept experiments to validate our theory as well.
|
[
"['Naman Agarwal' 'Pranjal Awasthi' 'Satyen Kale' 'Eric Zhao']"
] |
null | null |
2403.04990
| null | null |
http://arxiv.org/pdf/2403.04990v2
|
2024-03-12T06:35:49Z
|
2024-03-08T02:02:23Z
|
Jet Discrimination with Quantum Complete Graph Neural Network
|
Machine learning, particularly deep neural networks, has been widely utilized in high energy physics and has shown remarkable results in various applications. Moreover, the concept of machine learning has been extended to quantum computers, giving rise to a new research area known as quantum machine learning. In this paper, we propose a novel variational quantum circuit model, Quantum Complete Graph Neural Network (QCGNN), designed for learning complete graphs. We argue that QCGNN has a polynomial speedup against its classical counterpart, due to the property of quantum parallelism. In this paper, we study the application of QCGNN through the challenging jet discrimination, where the jets are represented with complete graphs. Subsequently, we conduct a comparative analysis with classical graph neural networks to establish a benchmark.
|
[
"['Yi-An Chen' 'Kai-Feng Chen']"
] |
null | null |
2403.05004
| null | null |
http://arxiv.org/pdf/2403.05004v1
|
2024-03-08T03:03:20Z
|
2024-03-08T03:03:20Z
|
Can't Remember Details in Long Documents? You Need Some R&R
|
Long-context large language models (LLMs) hold promise for tasks such as question-answering (QA) over long documents, but they tend to miss important information in the middle of context documents (arXiv:2307.03172v3). Here, we introduce $textit{R&R}$ -- a combination of two novel prompt-based methods called $textit{reprompting}$ and $textit{in-context retrieval}$ (ICR) -- to alleviate this effect in document-based QA. In reprompting, we repeat the prompt instructions periodically throughout the context document to remind the LLM of its original task. In ICR, rather than instructing the LLM to answer the question directly, we instruct it to retrieve the top $k$ passage numbers most relevant to the given question, which are then used as an abbreviated context in a second QA prompt. We test R&R with GPT-4 Turbo and Claude-2.1 on documents up to 80k tokens in length and observe a 16-point boost in QA accuracy on average. Our further analysis suggests that R&R improves performance on long document-based QA because it reduces the distance between relevant context and the instructions. Finally, we show that compared to short-context chunkwise methods, R&R enables the use of larger chunks that cost fewer LLM calls and output tokens, while minimizing the drop in accuracy.
|
[
"['Devanshu Agrawal' 'Shang Gao' 'Martin Gajek']"
] |
null | null |
2403.05006
| null | null |
http://arxiv.org/pdf/2403.05006v1
|
2024-03-08T03:05:11Z
|
2024-03-08T03:05:11Z
|
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback
|
Reinforcement learning with human feedback (RLHF) is an emerging paradigm to align models with human preferences. Typically, RLHF aggregates preferences from multiple individuals who have diverse viewpoints that may conflict with each other. Our work textit{initiates} the theoretical study of multi-party RLHF that explicitly models the diverse preferences of multiple individuals. We show how traditional RLHF approaches can fail since learning a single reward function cannot capture and balance the preferences of multiple individuals. To overcome such limitations, we incorporate meta-learning to learn multiple preferences and adopt different social welfare functions to aggregate the preferences across multiple parties. We focus on the offline learning setting and establish sample complexity bounds, along with efficiency and fairness guarantees, for optimizing diverse social welfare functions such as Nash, Utilitarian, and Leximin welfare functions. Our results show a separation between the sample complexities of multi-party RLHF and traditional single-party RLHF. Furthermore, we consider a reward-free setting, where each individual's preference is no longer consistent with a reward model, and give pessimistic variants of the von Neumann Winner based on offline preference data. Taken together, our work showcases the advantage of multi-party RLHF but also highlights its more demanding statistical complexity.
|
[
"['Huiying Zhong' 'Zhun Deng' 'Weijie J. Su' 'Zhiwei Steven Wu'\n 'Linjun Zhang']"
] |
null | null |
2403.05014
| null | null |
http://arxiv.org/pdf/2403.05014v1
|
2024-03-08T03:27:58Z
|
2024-03-08T03:27:58Z
|
Simple Multigraph Convolution Networks
|
Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators. To alleviate this problem, this paper proposes a Simple MultiGraph Convolution Networks (SMGCN) which first extracts consistent cross-view topology from multigraphs including edge-level and subgraph-level topology, then performs polynomial expansion based on raw multigraphs and consistent topologies. In theory, SMGCN utilizes the consistent topologies in polynomial expansion rather than standard cross-view polynomial expansion, which performs credible cross-view spatial message-passing, follows the spectral convolution paradigm, and effectively reduces the complexity of standard polynomial expansion. In the simulations, experimental results demonstrate that SMGCN achieves state-of-the-art performance on ACM and DBLP multigraph benchmark datasets. Our codes are available at https://github.com/frinkleko/SMGCN.
|
[
"['Danyang Wu' 'Xinjie Shen' 'Jitao Lu' 'Jin Xu' 'Feiping Nie']"
] |
null | null |
2403.05024
| null | null |
http://arxiv.org/pdf/2403.05024v1
|
2024-03-08T04:02:34Z
|
2024-03-08T04:02:34Z
|
A Probabilistic Hadamard U-Net for MRI Bias Field Correction
|
Magnetic field inhomogeneity correction remains a challenging task in MRI analysis. Most established techniques are designed for brain MRI by supposing that image intensities in the identical tissue follow a uniform distribution. Such an assumption cannot be easily applied to other organs, especially those that are small in size and heterogeneous in texture (large variations in intensity), such as the prostate. To address this problem, this paper proposes a probabilistic Hadamard U-Net (PHU-Net) for prostate MRI bias field correction. First, a novel Hadamard U-Net (HU-Net) is introduced to extract the low-frequency scalar field, multiplied by the original input to obtain the prototypical corrected image. HU-Net converts the input image from the time domain into the frequency domain via Hadamard transform. In the frequency domain, high-frequency components are eliminated using the trainable filter (scaling layer), hard-thresholding layer, and sparsity penalty. Next, a conditional variational autoencoder is used to encode possible bias field-corrected variants into a low-dimensional latent space. Random samples drawn from latent space are then incorporated with a prototypical corrected image to generate multiple plausible images. Experimental results demonstrate the effectiveness of PHU-Net in correcting bias-field in prostate MRI with a fast inference speed. It has also been shown that prostate MRI segmentation accuracy improves with the high-quality corrected images from PHU-Net. The code will be available in the final version of this manuscript.
|
[
"['Xin Zhu' 'Hongyi Pan' 'Yury Velichko' 'Adam B. Murphy' 'Ashley Ross'\n 'Baris Turkbey' 'Ahmet Enis Cetin' 'Ulas Bagci']"
] |
null | null |
2403.05026
| null | null |
http://arxiv.org/pdf/2403.05026v1
|
2024-03-08T04:07:23Z
|
2024-03-08T04:07:23Z
|
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
|
Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution settings only focuses on the time domain, failing to handle cases involving distribution shifts in the spectral domain. In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time. However, this investigation poses two key challenges: i) it is non-trivial to capture different graph patterns that are driven by various frequency components entangled in the spectral domain; and ii) it remains unclear how to handle distribution shifts with the discovered spectral patterns. To address these challenges, we propose Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts (SILD), which can handle distribution shifts on dynamic graphs by capturing and utilizing invariant and variant spectral patterns. Specifically, we first design a DyGNN with Fourier transform to obtain the ego-graph trajectory spectrums, allowing the mixed dynamic graph patterns to be transformed into separate frequency components. We then develop a disentangled spectrum mask to filter graph dynamics from various frequency components and discover the invariant and variant spectral patterns. Finally, we propose invariant spectral filtering, which encourages the model to rely on invariant patterns for generalization under distribution shifts. Experimental results on synthetic and real-world dynamic graph datasets demonstrate the superiority of our method for both node classification and link prediction tasks under distribution shifts.
|
[
"['Zeyang Zhang' 'Xin Wang' 'Ziwei Zhang' 'Zhou Qin' 'Weigao Wen' 'Hui Xue'\n 'Haoyang Li' 'Wenwu Zhu']"
] |
null | null |
2403.05030
| null | null |
http://arxiv.org/pdf/2403.05030v3
|
2024-04-01T21:32:18Z
|
2024-03-08T04:22:48Z
|
Defending Against Unforeseen Failure Modes with Latent Adversarial
Training
|
Despite extensive diagnostics and debugging by developers, AI systems sometimes exhibit harmful unintended behaviors. Finding and fixing these is challenging because the attack surface is so large -- it is not tractable to exhaustively search for inputs that may elicit harmful behaviors. Red-teaming and adversarial training (AT) are commonly used to improve robustness, however, they empirically struggle to fix failure modes that differ from the attacks used during training. In this work, we utilize latent adversarial training (LAT) to defend against vulnerabilities without generating inputs that elicit them. LAT leverages the compressed, abstract, and structured latent representations of concepts that the network actually uses for prediction. We use it to remove trojans and defend against held-out classes of adversarial attacks. We show in image classification, text classification, and text generation tasks that LAT usually improves both robustness to novel attacks and performance on clean data relative to AT. This suggests that LAT can be a promising tool for defending against failure modes that are not explicitly identified by developers.
|
[
"['Stephen Casper' 'Lennart Schulze' 'Oam Patel' 'Dylan Hadfield-Menell']"
] |
null | null |
2403.05033
| null | null |
http://arxiv.org/pdf/2403.05033v1
|
2024-03-08T04:23:50Z
|
2024-03-08T04:23:50Z
|
Quantifying Manifolds: Do the manifolds learned by Generative
Adversarial Networks converge to the real data manifold
|
This paper presents our experiments to quantify the manifolds learned by ML models (in our experiment, we use a GAN model) as they train. We compare the manifolds learned at each epoch to the real manifolds representing the real data. To quantify a manifold, we study the intrinsic dimensions and topological features of the manifold learned by the ML model, how these metrics change as we continue to train the model, and whether these metrics convergence over the course of training to the metrics of the real data manifold.
|
[
"['Anupam Chaudhuri' 'Anj Simmons' 'Mohamed Abdelrazek']"
] |
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