categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.04896 | null | null | http://arxiv.org/pdf/2406.04896v1 | 2024-06-07T12:43:17Z | 2024-06-07T12:43:17Z | Stabilizing Extreme Q-learning by Maclaurin Expansion | In Extreme Q-learning (XQL), Gumbel Regression is performed with an assumed Gumbel distribution for the error distribution. This allows learning of the value function without sampling out-of-distribution actions and has shown excellent performance mainly in Offline RL. However, issues remained, including the exponential term in the loss function causing instability and the potential for an error distribution diverging from the Gumbel distribution. Therefore, we propose Maclaurin Expanded Extreme Q-learning to enhance stability. In this method, applying Maclaurin expansion to the loss function in XQL enhances stability against large errors. It also allows adjusting the error distribution assumption from normal to Gumbel based on the expansion order. Our method significantly stabilizes learning in Online RL tasks from DM Control, where XQL was previously unstable. Additionally, it improves performance in several Offline RL tasks from D4RL, where XQL already showed excellent results. | [
"['Motoki Omura' 'Takayuki Osa' 'Yusuke Mukuta' 'Tatsuya Harada']"
] |
null | null | 2406.04897 | null | null | http://arxiv.org/pdf/2406.04897v1 | 2024-06-07T12:45:12Z | 2024-06-07T12:45:12Z | From Link Prediction to Forecasting: Information Loss in Batch-based
Temporal Graph Learning | Dynamic link prediction is an important problem considered by many recent works proposing various approaches for learning temporal edge patterns. To assess their efficacy, models are evaluated on publicly available benchmark datasets involving continuous-time and discrete-time temporal graphs. However, as we show in this work, the suitability of common batch-oriented evaluation depends on the datasets' characteristics, which can cause two issues: First, for continuous-time temporal graphs, fixed-size batches create time windows with different durations, resulting in an inconsistent dynamic link prediction task. Second, for discrete-time temporal graphs, the sequence of batches can additionally introduce temporal dependencies that are not present in the data. In this work, we empirically show that this common evaluation approach leads to skewed model performance and hinders the fair comparison of methods. We mitigate this problem by reformulating dynamic link prediction as a link forecasting task that better accounts for temporal information present in the data. We provide implementations of our new evaluation method for commonly used graph learning frameworks. | [
"['Moritz Lampert' 'Christopher Blöcker' 'Ingo Scholtes']"
] |
null | null | 2406.04903 | null | null | http://arxiv.org/pdf/2406.04903v1 | 2024-06-07T12:54:50Z | 2024-06-07T12:54:50Z | Concept Drift Detection using Ensemble of Integrally Private Models | Deep neural networks (DNNs) are one of the most widely used machine learning algorithm. DNNs requires the training data to be available beforehand with true labels. This is not feasible for many real-world problems where data arrives in the streaming form and acquisition of true labels are scarce and expensive. In the literature, not much focus has been given to the privacy prospect of the streaming data, where data may change its distribution frequently. These concept drifts must be detected privately in order to avoid any disclosure risk from DNNs. Existing privacy models use concept drift detection schemes such ADWIN, KSWIN to detect the drifts. In this paper, we focus on the notion of integrally private DNNs to detect concept drifts. Integrally private DNNs are the models which recur frequently from different datasets. Based on this, we introduce an ensemble methodology which we call 'Integrally Private Drift Detection' (IPDD) method to detect concept drift from private models. Our IPDD method does not require labels to detect drift but assumes true labels are available once the drift has been detected. We have experimented with binary and multi-class synthetic and real-world data. Our experimental results show that our methodology can privately detect concept drift, has comparable utility (even better in some cases) with ADWIN and outperforms utility from different levels of differentially private models. The source code for the paper is available hyperlink{https://github.com/Ayush-Umu/Concept-drift-detection-Using-Integrally-private-models}{here}. | [
"['Ayush K. Varshney' 'Vicenc Torra']"
] |
null | null | 2406.04910 | null | null | http://arxiv.org/pdf/2406.04910v1 | 2024-06-07T13:00:57Z | 2024-06-07T13:00:57Z | PolyLUT-Add: FPGA-based LUT Inference with Wide Inputs | FPGAs have distinct advantages as a technology for deploying deep neural networks (DNNs) at the edge. Lookup Table (LUT) based networks, where neurons are directly modelled using LUTs, help maximize this promise of offering ultra-low latency and high area efficiency on FPGAs. Unfortunately, LUT resource usage scales exponentially with the number of inputs to the LUT, restricting PolyLUT to small LUT sizes. This work introduces PolyLUT-Add, a technique that enhances neuron connectivity by combining $A$ PolyLUT sub-neurons via addition to improve accuracy. Moreover, we describe a novel architecture to improve its scalability. We evaluated our implementation over the MNIST, Jet Substructure classification and Network Intrusion Detection benchmark and found that for similar accuracy, PolyLUT-Add achieves a LUT reduction of $1.3-7.7times$ with a $1.2-2.2times$ decrease in latency. | [
"['Binglei Lou' 'Richard Rademacher' 'David Boland' 'Philip H. W. Leong']"
] |
null | null | 2406.04913 | null | null | http://arxiv.org/pdf/2406.04913v1 | 2024-06-07T13:09:48Z | 2024-06-07T13:09:48Z | Online Adaptation for Enhancing Imitation Learning Policies | Imitation learning enables autonomous agents to learn from human examples, without the need for a reward signal. Still, if the provided dataset does not encapsulate the task correctly, or when the task is too complex to be modeled, such agents fail to reproduce the expert policy. We propose to recover from these failures through online adaptation. Our approach combines the action proposal coming from a pre-trained policy with relevant experience recorded by an expert. The combination results in an adapted action that closely follows the expert. Our experiments show that an adapted agent performs better than its pure imitation learning counterpart. Notably, adapted agents can achieve reasonable performance even when the base, non-adapted policy catastrophically fails. | [
"['Federico Malato' 'Ville Hautamaki']"
] |
null | null | 2406.04914 | null | null | http://arxiv.org/pdf/2406.04914v1 | 2024-06-07T13:11:04Z | 2024-06-07T13:11:04Z | Submodular Framework for Structured-Sparse Optimal Transport | Unbalanced optimal transport (UOT) has recently gained much attention due to its flexible framework for handling un-normalized measures and its robustness properties. In this work, we explore learning (structured) sparse transport plans in the UOT setting, i.e., transport plans have an upper bound on the number of non-sparse entries in each column (structured sparse pattern) or in the whole plan (general sparse pattern). We propose novel sparsity-constrained UOT formulations building on the recently explored maximum mean discrepancy based UOT. We show that the proposed optimization problem is equivalent to the maximization of a weakly submodular function over a uniform matroid or a partition matroid. We develop efficient gradient-based discrete greedy algorithms and provide the corresponding theoretical guarantees. Empirically, we observe that our proposed greedy algorithms select a diverse support set and we illustrate the efficacy of the proposed approach in various applications. | [
"['Piyushi Manupriya' 'Pratik Jawanpuria' 'Karthik S. Gurumoorthy'\n 'SakethaNath Jagarlapudi' 'Bamdev Mishra']"
] |
null | null | 2406.04916 | null | null | http://arxiv.org/pdf/2406.04916v1 | 2024-06-07T13:16:10Z | 2024-06-07T13:16:10Z | Combinatorial Complex Score-based Diffusion Modelling through Stochastic
Differential Equations | Graph structures offer a versatile framework for representing diverse patterns in nature and complex systems, applicable across domains like molecular chemistry, social networks, and transportation systems. While diffusion models have excelled in generating various objects, generating graphs remains challenging. This thesis explores the potential of score-based generative models in generating such objects through a modelization as combinatorial complexes, which are powerful topological structures that encompass higher-order relationships. In this thesis, we propose a unified framework by employing stochastic differential equations. We not only generalize the generation of complex objects such as graphs and hypergraphs, but we also unify existing generative modelling approaches such as Score Matching with Langevin dynamics and Denoising Diffusion Probabilistic Models. This innovation overcomes limitations in existing frameworks that focus solely on graph generation, opening up new possibilities in generative AI. The experiment results showed that our framework could generate these complex objects, and could also compete against state-of-the-art approaches for mere graph and molecule generation tasks. | [
"['Adrien Carrel']"
] |
null | null | 2406.04920 | null | null | http://arxiv.org/pdf/2406.04920v1 | 2024-06-07T13:24:19Z | 2024-06-07T13:24:19Z | Sim-to-real Transfer of Deep Reinforcement Learning Agents for Online
Coverage Path Planning | Sim-to-real transfer presents a difficult challenge, where models trained in simulation are to be deployed in the real world. The distribution shift between the two settings leads to biased representations of the perceived real-world environment, and thus to suboptimal predictions. In this work, we tackle the challenge of sim-to-real transfer of reinforcement learning (RL) agents for coverage path planning (CPP). In CPP, the task is for a robot to find a path that visits every point of a confined area. Specifically, we consider the case where the environment is unknown, and the agent needs to plan the path online while mapping the environment. We bridge the sim-to-real gap through a semi-virtual environment with a simulated sensor and obstacles, while including real robot kinematics and real-time aspects. We investigate what level of fine-tuning is needed for adapting to a realistic setting, comparing to an agent trained solely in simulation. We find that a high model inference frequency is sufficient for reducing the sim-to-real gap, while fine-tuning degrades performance initially. By training the model in simulation and deploying it at a high inference frequency, we transfer state-of-the-art results from simulation to the real domain, where direct learning would take in the order of weeks with manual interaction, i.e., would be completely infeasible. | [
"['Arvi Jonnarth' 'Ola Johansson' 'Michael Felsberg']"
] |
null | null | 2406.04926 | null | null | http://arxiv.org/pdf/2406.04926v1 | 2024-06-07T13:31:51Z | 2024-06-07T13:31:51Z | Through the Thicket: A Study of Number-Oriented LLMs derived from Random
Forest Models | Large Language Models (LLMs) have shown exceptional performance in text processing. Notably, LLMs can synthesize information from large datasets and explain their decisions similarly to human reasoning through a chain of thought (CoT). An emerging application of LLMs is the handling and interpreting of numerical data, where fine-tuning enhances their performance over basic inference methods. This paper proposes a novel approach to training LLMs using knowledge transfer from a random forest (RF) ensemble, leveraging its efficiency and accuracy. By converting RF decision paths into natural language statements, we generate outputs for LLM fine-tuning, enhancing the model's ability to classify and explain its decisions. Our method includes verifying these rules through established classification metrics, ensuring their correctness. We also examine the impact of preprocessing techniques on the representation of numerical data and their influence on classification accuracy and rule correctness | [
"['Michał Romaszewski' 'Przemysław Sekuła' 'Przemysław Głomb'\n 'Michał Cholewa' 'Katarzyna Kołodziej']"
] |
null | null | 2406.04928 | null | null | http://arxiv.org/pdf/2406.04928v1 | 2024-06-07T13:34:17Z | 2024-06-07T13:34:17Z | AGBD: A Global-scale Biomass Dataset | Accurate estimates of Above Ground Biomass (AGB) are essential in addressing two of humanity's biggest challenges, climate change and biodiversity loss. Existing datasets for AGB estimation from satellite imagery are limited. Either they focus on specific, local regions at high resolution, or they offer global coverage at low resolution. There is a need for a machine learning-ready, globally representative, high-resolution benchmark. Our findings indicate significant variability in biomass estimates across different vegetation types, emphasizing the necessity for a dataset that accurately captures global diversity. To address these gaps, we introduce a comprehensive new dataset that is globally distributed, covers a range of vegetation types, and spans several years. This dataset combines AGB reference data from the GEDI mission with data from Sentinel-2 and PALSAR-2 imagery. Additionally, it includes pre-processed high-level features such as a dense canopy height map, an elevation map, and a land-cover classification map. We also produce a dense, high-resolution (10m) map of AGB predictions for the entire area covered by the dataset. Rigorously tested, our dataset is accompanied by several benchmark models and is publicly available. It can be easily accessed using a single line of code, offering a solid basis for efforts towards global AGB estimation. The GitHub repository github.com/ghjuliasialelli/AGBD serves as a one-stop shop for all code and data. | [
"['Ghjulia Sialelli' 'Torben Peters' 'Jan D. Wegner' 'Konrad Schindler']"
] |
null | null | 2406.04929 | null | null | http://arxiv.org/pdf/2406.04929v1 | 2024-06-07T13:35:08Z | 2024-06-07T13:35:08Z | Protein pathways as a catalyst to directed evolution of the topology of
artificial neural networks | In the present article, we propose a paradigm shift on evolving Artificial Neural Networks (ANNs) towards a new bio-inspired design that is grounded on the structural properties, interactions, and dynamics of protein networks (PNs): the Artificial Protein Network (APN). This introduces several advantages previously unrealized by state-of-the-art approaches in NE: (1) We can draw inspiration from how nature, thanks to millions of years of evolution, efficiently encodes protein interactions in the DNA to translate our APN to silicon DNA. This helps bridge the gap between syntax and semantics observed in current NE approaches. (2) We can learn from how nature builds networks in our genes, allowing us to design new and smarter networks through EA evolution. (3) We can perform EA crossover/mutation operations and evolution steps, replicating the operations observed in nature directly on the genotype of networks, thus exploring and exploiting the phenotypic space, such that we avoid getting trapped in sub-optimal solutions. (4) Our novel definition of APN opens new ways to leverage our knowledge about different living things and processes from biology. (5) Using biologically inspired encodings, we can model more complex demographic and ecological relationships (e.g., virus-host or predator-prey interactions), allowing us to optimise for multiple, often conflicting objectives. | [
"['Oscar Lao' 'Konstantinos Zacharopoulos' 'Apostolos Fournaris'\n 'Rossano Schifanella' 'Ioannis Arapakis']"
] |
null | null | 2406.04932 | null | null | http://arxiv.org/pdf/2406.04932v1 | 2024-06-07T13:37:36Z | 2024-06-07T13:37:36Z | Faster Than Lies: Real-time Deepfake Detection using Binary Neural
Networks | Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover, our method incorporates Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake, DFFD, and CIFAKE datasets demonstrate our method's state-of-the-art performance in most scenarios with a significant efficiency gain of up to a $20times$ reduction in FLOPs during inference. Finally, by exploring BNNs in deepfake detection to balance accuracy and efficiency, this work paves the way for future research on efficient deepfake detection. | [
"['Lanzino Romeo' 'Fontana Federico' 'Diko Anxhelo' 'Marini Marco Raoul'\n 'Cinque Luigi']"
] |
null | null | 2406.04934 | null | null | http://arxiv.org/pdf/2406.04934v1 | 2024-06-07T13:41:17Z | 2024-06-07T13:41:17Z | Optimal Recurrent Network Topologies for Dynamical Systems
Reconstruction | In dynamical systems reconstruction (DSR) we seek to infer from time series measurements a generative model of the underlying dynamical process. This is a prime objective in any scientific discipline, where we are particularly interested in parsimonious models with a low parameter load. A common strategy here is parameter pruning, removing all parameters with small weights. However, here we find this strategy does not work for DSR, where even low magnitude parameters can contribute considerably to the system dynamics. On the other hand, it is well known that many natural systems which generate complex dynamics, like the brain or ecological networks, have a sparse topology with comparatively few links. Inspired by this, we show that geometric pruning, where in contrast to magnitude-based pruning weights with a low contribution to an attractor's geometrical structure are removed, indeed manages to reduce parameter load substantially without significantly hampering DSR quality. We further find that the networks resulting from geometric pruning have a specific type of topology, and that this topology, and not the magnitude of weights, is what is most crucial to performance. We provide an algorithm that automatically generates such topologies which can be used as priors for generative modeling of dynamical systems by RNNs, and compare it to other well studied topologies like small-world or scale-free networks. | [
"['Christoph Jürgen Hemmer' 'Manuel Brenner' 'Florian Hess'\n 'Daniel Durstewitz']"
] |
null | null | 2406.04935 | null | null | http://arxiv.org/pdf/2406.04935v1 | 2024-06-07T13:42:15Z | 2024-06-07T13:42:15Z | SLOPE: Search with Learned Optimal Pruning-based Expansion | Heuristic search is often used for motion planning and pathfinding problems, for finding the shortest path in a graph while also promising completeness and optimal efficiency. The drawback is it's space complexity, specifically storing all expanded child nodes in memory and sorting large lists of active nodes, which can be a problem in real-time scenarios with limited on-board computation. To combat this, we present the Search with Learned Optimal Pruning-based Expansion (SLOPE), which, learns the distance of a node from a possible optimal path, unlike other approaches that learn a cost-to-go value. The unfavored nodes are then pruned according to the said distance, which in turn reduces the size of the open list. This ensures that the search explores only the region close to optimal paths while lowering memory and computational costs. Unlike traditional learning methods, our approach is orthogonal to estimating cost-to-go heuristics, offering a complementary strategy for improving search efficiency. We demonstrate the effectiveness of our approach evaluating it as a standalone search method and in conjunction with learned heuristic functions, achieving comparable-or-better node expansion metrics, while lowering the number of child nodes in the open list. Our code is available at https://github.com/dbokan1/SLOPE. | [
"['Davor Bokan' 'Zlatan Ajanovic' 'Bakir Lacevic']"
] |
null | null | 2406.04938 | null | null | http://arxiv.org/pdf/2406.04938v1 | 2024-06-07T13:46:23Z | 2024-06-07T13:46:23Z | SpanGNN: Towards Memory-Efficient Graph Neural Networks via Spanning
Subgraph Training | Graph Neural Networks (GNNs) have superior capability in learning graph data. Full-graph GNN training generally has high accuracy, however, it suffers from large peak memory usage and encounters the Out-of-Memory problem when handling large graphs. To address this memory problem, a popular solution is mini-batch GNN training. However, mini-batch GNN training increases the training variance and sacrifices the model accuracy. In this paper, we propose a new memory-efficient GNN training method using spanning subgraph, called SpanGNN. SpanGNN trains GNN models over a sequence of spanning subgraphs, which are constructed from empty structure. To overcome the excessive peak memory consumption problem, SpanGNN selects a set of edges from the original graph to incrementally update the spanning subgraph between every epoch. To ensure the model accuracy, we introduce two types of edge sampling strategies (i.e., variance-reduced and noise-reduced), and help SpanGNN select high-quality edges for the GNN learning. We conduct experiments with SpanGNN on widely used datasets, demonstrating SpanGNN's advantages in the model performance and low peak memory usage. | [
"['Xizhi Gu' 'Hongzheng Li' 'Shihong Gao' 'Xinyan Zhang' 'Lei Chen'\n 'Yingxia Shao']"
] |
null | null | 2406.04940 | null | null | http://arxiv.org/pdf/2406.04940v1 | 2024-06-07T13:47:40Z | 2024-06-07T13:47:40Z | CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling | Terrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO$_2$ emissions. The importance of predicting carbon fluxes has led to the emerging field of data-driven carbon flux modelling (DDCFM), which uses statistical techniques to predict carbon fluxes from biophysical data. However, the field lacks a standardized dataset to promote comparisons between models. To address this gap, we present CarbonSense, the first machine learning-ready dataset for DDCFM. CarbonSense integrates measured carbon fluxes, meteorological predictors, and satellite imagery from 385 locations across the globe, offering comprehensive coverage and facilitating robust model training. Additionally, we provide a baseline model using a current state-of-the-art DDCFM approach and a novel transformer based model. Our experiments illustrate the potential gains that multimodal deep learning techniques can bring to this domain. By providing these resources, we aim to lower the barrier to entry for other deep learning researchers to develop new models and drive new advances in carbon flux modelling. | [
"['Matthew Fortier' 'Mats L. Richter' 'Oliver Sonnentag' 'Chris Pal']"
] |
null | null | 2406.04943 | null | null | http://arxiv.org/pdf/2406.04943v1 | 2024-06-07T13:53:24Z | 2024-06-07T13:53:24Z | Multiple-input, multiple-output modal testing of a Hawk T1A aircraft: A
new full-scale dataset for structural health monitoring | The use of measured vibration data from structures has a long history of enabling the development of methods for inference and monitoring. In particular, applications based on system identification and structural health monitoring have risen to prominence over recent decades and promise significant benefits when implemented in practice. However, significant challenges remain in the development of these methods. The introduction of realistic, full-scale datasets will be an important contribution to overcoming these challenges. This paper presents a new benchmark dataset capturing the dynamic response of a decommissioned BAE Systems Hawk T1A. The dataset reflects the behaviour of a complex structure with a history of service that can still be tested in controlled laboratory conditions, using a variety of known loading and damage simulation conditions. As such, it provides a key stepping stone between simple laboratory test structures and in-service structures. In this paper, the Hawk structure is described in detail, alongside a comprehensive summary of the experimental work undertaken. Following this, key descriptive highlights of the dataset are presented, before a discussion of the research challenges that the data present. Using the dataset, non-linearity in the structure is demonstrated, as well as the sensitivity of the structure to damage of different types. The dataset is highly applicable to many academic enquiries and additional analysis techniques which will enable further advancement of vibration-based engineering techniques. | [
"['James Wilson' 'Max D. Champneys' 'Matt Tipuric' 'Robin Mills'\n 'David J. Wagg' 'Timothy J. Rogers']"
] |
null | null | 2406.04949 | null | null | http://arxiv.org/pdf/2406.04949v1 | 2024-06-07T14:07:23Z | 2024-06-07T14:07:23Z | Nacala-Roof-Material: Drone Imagery for Roof Detection, Classification,
and Segmentation to Support Mosquito-borne Disease Risk Assessment | As low-quality housing and in particular certain roof characteristics are associated with an increased risk of malaria, classification of roof types based on remote sensing imagery can support the assessment of malaria risk and thereby help prevent the disease. To support research in this area, we release the Nacala-Roof-Material dataset, which contains high-resolution drone images from Mozambique with corresponding labels delineating houses and specifying their roof types. The dataset defines a multi-task computer vision problem, comprising object detection, classification, and segmentation. In addition, we benchmarked various state-of-the-art approaches on the dataset. Canonical U-Nets, YOLOv8, and a custom decoder on pretrained DINOv2 served as baselines. We show that each of the methods has its advantages but none is superior on all tasks, which highlights the potential of our dataset for future research in multi-task learning. While the tasks are closely related, accurate segmentation of objects does not necessarily imply accurate instance separation, and vice versa. We address this general issue by introducing a variant of the deep ordinal watershed (DOW) approach that additionally separates the interior of objects, allowing for improved object delineation and separation. We show that our DOW variant is a generic approach that improves the performance of both U-Net and DINOv2 backbones, leading to a better trade-off between semantic segmentation and instance segmentation. | [
"['Venkanna Babu Guthula' 'Stefan Oehmcke' 'Remigio Chilaule' 'Hui Zhang'\n 'Nico Lang' 'Ankit Kariryaa' 'Johan Mottelson' 'Christian Igel']"
] |
null | null | 2406.04963 | null | null | http://arxiv.org/pdf/2406.04963v1 | 2024-06-07T14:29:21Z | 2024-06-07T14:29:21Z | Learning Divergence Fields for Shift-Robust Graph Representations | Real-world data generation often involves certain geometries (e.g., graphs) that induce instance-level interdependence. This characteristic makes the generalization of learning models more difficult due to the intricate interdependent patterns that impact data-generative distributions and can vary from training to testing. In this work, we propose a geometric diffusion model with learnable divergence fields for the challenging generalization problem with interdependent data. We generalize the diffusion equation with stochastic diffusivity at each time step, which aims to capture the multi-faceted information flows among interdependent data. Furthermore, we derive a new learning objective through causal inference, which can guide the model to learn generalizable patterns of interdependence that are insensitive across domains. Regarding practical implementation, we introduce three model instantiations that can be considered as the generalized versions of GCN, GAT, and Transformers, respectively, which possess advanced robustness against distribution shifts. We demonstrate their promising efficacy for out-of-distribution generalization on diverse real-world datasets. | [
"['Qitian Wu' 'Fan Nie' 'Chenxiao Yang' 'Junchi Yan']"
] |
null | null | 2406.04964 | null | null | http://arxiv.org/pdf/2406.04964v1 | 2024-06-07T14:29:30Z | 2024-06-07T14:29:30Z | Neural Laplace for learning Stochastic Differential Equations | Neural Laplace is a unified framework for learning diverse classes of differential equations (DE). For different classes of DE, this framework outperforms other approaches relying on neural networks that aim to learn classes of ordinary differential equations (ODE). However, many systems can't be modelled using ODEs. Stochastic differential equations (SDE) are the mathematical tool of choice when modelling spatiotemporal DE dynamics under the influence of randomness. In this work, we review the potential applications of Neural Laplace to learn diverse classes of SDE, both from a theoretical and a practical point of view. | [
"['Adrien Carrel']"
] |
null | null | 2406.04975 | null | null | http://arxiv.org/pdf/2406.04975v1 | 2024-06-07T14:39:28Z | 2024-06-07T14:39:28Z | UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies
for Multivariate Time Series Forecasting | Transformer-based models have emerged as powerful tools for multivariate time series forecasting (MTSF). However, existing Transformer models often fall short of capturing both intricate dependencies across variate and temporal dimensions in MTS data. Some recent models are proposed to separately capture variate and temporal dependencies through either two sequential or parallel attention mechanisms. However, these methods cannot directly and explicitly learn the intricate inter-series and intra-series dependencies. In this work, we first demonstrate that these dependencies are very important as they usually exist in real-world data. To directly model these dependencies, we propose a transformer-based model UniTST containing a unified attention mechanism on the flattened patch tokens. Additionally, we add a dispatcher module which reduces the complexity and makes the model feasible for a potentially large number of variates. Although our proposed model employs a simple architecture, it offers compelling performance as shown in our extensive experiments on several datasets for time series forecasting. | [
"['Juncheng Liu' 'Chenghao Liu' 'Gerald Woo' 'Yiwei Wang' 'Bryan Hooi'\n 'Caiming Xiong' 'Doyen Sahoo']"
] |
null | null | 2406.04981 | null | null | http://arxiv.org/pdf/2406.04981v1 | 2024-06-07T14:44:37Z | 2024-06-07T14:44:37Z | The Price of Implicit Bias in Adversarially Robust Generalization | We study the implicit bias of optimization in robust empirical risk minimization (robust ERM) and its connection with robust generalization. In classification settings under adversarial perturbations with linear models, we study what type of regularization should ideally be applied for a given perturbation set to improve (robust) generalization. We then show that the implicit bias of optimization in robust ERM can significantly affect the robustness of the model and identify two ways this can happen; either through the optimization algorithm or the architecture. We verify our predictions in simulations with synthetic data and experimentally study the importance of implicit bias in robust ERM with deep neural networks. | [
"['Nikolaos Tsilivis' 'Natalie Frank' 'Nathan Srebro' 'Julia Kempe']"
] |
null | null | 2406.04993 | null | null | http://arxiv.org/pdf/2406.04993v1 | 2024-06-07T15:04:59Z | 2024-06-07T15:04:59Z | Development and Validation of a Deep-Learning Model for Differential
Treatment Benefit Prediction for Adults with Major Depressive Disorder
Deployed in the Artificial Intelligence in Depression Medication Enhancement
(AIDME) Study | INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize treatment and improve outcomes, which was deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study. OBJECTIVES: 1) Develop a model capable of predicting probabilities of remission across multiple pharmacological treatments for adults with at least moderate major depression. 2) Validate model predictions and examine them for amplification of harmful biases. METHODS: Data from previous clinical trials of antidepressant medications were standardized into a common framework and included 9,042 adults with moderate to severe major depression. Feature selection retained 25 clinical and demographic variables. Using Bayesian optimization, a deep learning model was trained on the training set, refined using the validation set, and tested once on the held-out test set. RESULTS: In the evaluation on the held-out test set, the model demonstrated achieved an AUC of 0.65. The model outperformed a null model on the test set (p = 0.01). The model demonstrated clinical utility, achieving an absolute improvement in population remission rate in hypothetical and actual improvement testing. While the model did identify one drug (escitalopram) as generally outperforming the other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. On bias testing, the model did not amplify potentially harmful biases. CONCLUSIONS: We demonstrate the first model capable of predicting outcomes for 10 different treatment options for patients with MDD, intended to be used at or near the start of treatment to personalize treatment. The model was put into clinical practice during the AIDME randomized controlled trial whose results are reported separately. | [
"['David Benrimoh' 'Caitrin Armstrong' 'Joseph Mehltretter'\n 'Robert Fratila' 'Kelly Perlman' 'Sonia Israel' 'Adam Kapelner'\n 'Sagar V. Parikh' 'Jordan F. Karp' 'Katherine Heller' 'Gustavo Turecki']"
] |
null | null | 2406.04997 | null | null | http://arxiv.org/pdf/2406.04997v1 | 2024-06-07T15:07:07Z | 2024-06-07T15:07:07Z | On the social bias of speech self-supervised models | Self-supervised learning (SSL) speech models have achieved remarkable performance in various tasks, yet the biased outcomes, especially affecting marginalized groups, raise significant concerns. Social bias refers to the phenomenon where algorithms potentially amplify disparate properties between social groups present in the data used for training. Bias in SSL models can perpetuate injustice by automating discriminatory patterns and reinforcing inequitable systems. This work reveals that prevalent SSL models inadvertently acquire biased associations. We probe how various factors, such as model architecture, size, and training methodologies, influence the propagation of social bias within these models. Finally, we explore the efficacy of debiasing SSL models through regularization techniques, specifically via model compression. Our findings reveal that employing techniques such as row-pruning and training wider, shallower models can effectively mitigate social bias within SSL model. | [
"['Yi-Cheng Lin' 'Tzu-Quan Lin' 'Hsi-Che Lin' 'Andy T. Liu' 'Hung-yi Lee']"
] |
null | null | 2406.04998 | null | null | http://arxiv.org/pdf/2406.04998v2 | 2024-06-12T08:49:16Z | 2024-06-07T15:09:25Z | ADBA:Approximation Decision Boundary Approach for Black-Box Adversarial
Attacks | Many machine learning models are susceptible to adversarial attacks, with decision-based black-box attacks representing the most critical threat in real-world applications. These attacks are extremely stealthy, generating adversarial examples using hard labels obtained from the target machine learning model. This is typically realized by optimizing perturbation directions, guided by decision boundaries identified through query-intensive exact search, significantly limiting the attack success rate. This paper introduces a novel approach using the Approximation Decision Boundary (ADB) to efficiently and accurately compare perturbation directions without precisely determining decision boundaries. The effectiveness of our ADB approach (ADBA) hinges on promptly identifying suitable ADB, ensuring reliable differentiation of all perturbation directions. For this purpose, we analyze the probability distribution of decision boundaries, confirming that using the distribution's median value as ADB can effectively distinguish different perturbation directions, giving rise to the development of the ADBA-md algorithm. ADBA-md only requires four queries on average to differentiate any pair of perturbation directions, which is highly query-efficient. Extensive experiments on six well-known image classifiers clearly demonstrate the superiority of ADBA and ADBA-md over multiple state-of-the-art black-box attacks. The source code is available at https://github.com/BUPTAIOC/ADBA. | [
"['Feiyang Wang' 'Xingquan Zuo' 'Hai Huang' 'Gang Chen']"
] |
null | null | 2406.05014 | null | null | http://arxiv.org/pdf/2406.05014v1 | 2024-06-07T15:24:38Z | 2024-06-07T15:24:38Z | Root Cause Analysis of Outliers with Missing Structural Knowledge | Recent work conceptualized root cause analysis (RCA) of anomalies via quantitative contribution analysis using causal counterfactuals in structural causal models (SCMs). The framework comes with three practical challenges: (1) it requires the causal directed acyclic graph (DAG), together with an SCM, (2) it is statistically ill-posed since it probes regression models in regions of low probability density, (3) it relies on Shapley values which are computationally expensive to find. In this paper, we propose simplified, efficient methods of root cause analysis when the task is to identify a unique root cause instead of quantitative contribution analysis. Our proposed methods run in linear order of SCM nodes and they require only the causal DAG without counterfactuals. Furthermore, for those use cases where the causal DAG is unknown, we justify the heuristic of identifying root causes as the variables with the highest anomaly score. | [
"['Nastaran Okati' 'Sergio Hernan Garrido Mejia' 'William Roy Orchard'\n 'Patrick Blöbaum' 'Dominik Janzing']"
] |
null | null | 2406.05017 | null | null | http://arxiv.org/pdf/2406.05017v1 | 2024-06-07T15:33:48Z | 2024-06-07T15:33:48Z | Adaptively Learning to Select-Rank in Online Platforms | Ranking algorithms are fundamental to various online platforms across e-commerce sites to content streaming services. Our research addresses the challenge of adaptively ranking items from a candidate pool for heterogeneous users, a key component in personalizing user experience. We develop a user response model that considers diverse user preferences and the varying effects of item positions, aiming to optimize overall user satisfaction with the ranked list. We frame this problem within a contextual bandits framework, with each ranked list as an action. Our approach incorporates an upper confidence bound to adjust predicted user satisfaction scores and selects the ranking action that maximizes these adjusted scores, efficiently solved via maximum weight imperfect matching. We demonstrate that our algorithm achieves a cumulative regret bound of $O(dsqrt{NKT})$ for ranking $K$ out of $N$ items in a $d$-dimensional context space over $T$ rounds, under the assumption that user responses follow a generalized linear model. This regret alleviates dependence on the ambient action space, whose cardinality grows exponentially with $N$ and $K$ (thus rendering direct application of existing adaptive learning algorithms -- such as UCB or Thompson sampling -- infeasible). Experiments conducted on both simulated and real-world datasets demonstrate our algorithm outperforms the baseline. | [
"['Jingyuan Wang' 'Perry Dong' 'Ying Jin' 'Ruohan Zhan' 'Zhengyuan Zhou']"
] |
null | null | 2406.05020 | null | null | http://arxiv.org/pdf/2406.05020v1 | 2024-06-07T15:38:27Z | 2024-06-07T15:38:27Z | Scaling up Probabilistic PDE Simulators with Structured Volumetric
Information | Modeling real-world problems with partial differential equations (PDEs) is a prominent topic in scientific machine learning. Classic solvers for this task continue to play a central role, e.g. to generate training data for deep learning analogues. Any such numerical solution is subject to multiple sources of uncertainty, both from limited computational resources and limited data (including unknown parameters). Gaussian process analogues to classic PDE simulation methods have recently emerged as a framework to construct fully probabilistic estimates of all these types of uncertainty. So far, much of this work focused on theoretical foundations, and as such is not particularly data efficient or scalable. Here we propose a framework combining a discretization scheme based on the popular Finite Volume Method with complementary numerical linear algebra techniques. Practical experiments, including a spatiotemporal tsunami simulation, demonstrate substantially improved scaling behavior of this approach over previous collocation-based techniques. | [
"['Tim Weiland' 'Marvin Pförtner' 'Philipp Hennig']"
] |
null | null | 2406.05027 | null | null | http://arxiv.org/pdf/2406.05027v2 | 2024-06-17T00:54:09Z | 2024-06-07T15:44:33Z | Optimizing Automatic Differentiation with Deep Reinforcement Learning | Computing Jacobians with automatic differentiation is ubiquitous in many scientific domains such as machine learning, computational fluid dynamics, robotics and finance. Even small savings in the number of computations or memory usage in Jacobian computations can already incur massive savings in energy consumption and runtime. While there exist many methods that allow for such savings, they generally trade computational efficiency for approximations of the exact Jacobian. In this paper, we present a novel method to optimize the number of necessary multiplications for Jacobian computation by leveraging deep reinforcement learning (RL) and a concept called cross-country elimination while still computing the exact Jacobian. Cross-country elimination is a framework for automatic differentiation that phrases Jacobian accumulation as ordered elimination of all vertices on the computational graph where every elimination incurs a certain computational cost. We formulate the search for the optimal elimination order that minimizes the number of necessary multiplications as a single player game which is played by an RL agent. We demonstrate that this method achieves up to 33% improvements over state-of-the-art methods on several relevant tasks taken from diverse domains. Furthermore, we show that these theoretical gains translate into actual runtime improvements by providing a cross-country elimination interpreter in JAX that can efficiently execute the obtained elimination orders. | [
"['Jamie Lohoff' 'Emre Neftci']"
] |
null | null | 2406.05033 | null | null | http://arxiv.org/pdf/2406.05033v1 | 2024-06-07T15:53:06Z | 2024-06-07T15:53:06Z | Gradient Descent on Logistic Regression with Non-Separable Data and
Large Step Sizes | We study gradient descent (GD) dynamics on logistic regression problems with large, constant step sizes. For linearly-separable data, it is known that GD converges to the minimizer with arbitrarily large step sizes, a property which no longer holds when the problem is not separable. In fact, the behaviour can be much more complex -- a sequence of period-doubling bifurcations begins at the critical step size $2/lambda$, where $lambda$ is the largest eigenvalue of the Hessian at the solution. Using a smaller-than-critical step size guarantees convergence if initialized nearby the solution: but does this suffice globally? In one dimension, we show that a step size less than $1/lambda$ suffices for global convergence. However, for all step sizes between $1/lambda$ and the critical step size $2/lambda$, one can construct a dataset such that GD converges to a stable cycle. In higher dimensions, this is actually possible even for step sizes less than $1/lambda$. Our results show that although local convergence is guaranteed for all step sizes less than the critical step size, global convergence is not, and GD may instead converge to a cycle depending on the initialization. | [
"['Si Yi Meng' 'Antonio Orvieto' 'Daniel Yiming Cao' 'Christopher De Sa']"
] |
null | null | 2406.05036 | null | null | http://arxiv.org/pdf/2406.05036v1 | 2024-06-07T15:58:12Z | 2024-06-07T15:58:12Z | TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks | Time series forecasting has become an increasingly popular research area due to its critical applications in various real-world domains such as traffic management, weather prediction, and financial analysis. Despite significant advancements, existing models face notable challenges, including the necessity of manual hyperparameter tuning for different datasets, and difficulty in effectively distinguishing signal from redundant features in data characterized by strong seasonality. These issues hinder the generalization and practical application of time series forecasting models. To solve this issues, we propose an innovative time series forecasting model TimeSieve designed to address these challenges. Our approach employs wavelet transforms to preprocess time series data, effectively capturing multi-scale features without the need for additional parameters or manual hyperparameter tuning. Additionally, we introduce the information bottleneck theory that filters out redundant features from both detail and approximation coefficients, retaining only the most predictive information. This combination reduces significantly improves the model's accuracy. Extensive experiments demonstrate that our model outperforms existing state-of-the-art methods on 70% of the datasets, achieving higher predictive accuracy and better generalization across diverse datasets. Our results validate the effectiveness of our approach in addressing the key challenges in time series forecasting, paving the way for more reliable and efficient predictive models in practical applications. The code for our model is available at https://github.com/xll0328/TimeSieve. | [
"['Ninghui Feng' 'Songning Lai' 'Fobao Zhou' 'Zhenxiao Yin' 'Hang Zhao']"
] |
null | null | 2406.05038 | null | null | http://arxiv.org/pdf/2406.05038v1 | 2024-06-07T16:02:07Z | 2024-06-07T16:02:07Z | Efficient 3D Shape Generation via Diffusion Mamba with Bidirectional
SSMs | Recent advancements in sequence modeling have led to the development of the Mamba architecture, noted for its selective state space approach, offering a promising avenue for efficient long sequence handling. However, its application in 3D shape generation, particularly at high resolutions, remains underexplored. Traditional diffusion transformers (DiT) with self-attention mechanisms, despite their potential, face scalability challenges due to the cubic complexity of attention operations as input length increases. This complexity becomes a significant hurdle when dealing with high-resolution voxel sizes. To address this challenge, we introduce a novel diffusion architecture tailored for 3D point clouds generation-Diffusion Mamba (DiM-3D). This architecture forgoes traditional attention mechanisms, instead utilizing the inherent efficiency of the Mamba architecture to maintain linear complexity with respect to sequence length. DiM-3D is characterized by fast inference times and substantially lower computational demands, quantified in reduced Gflops, thereby addressing the key scalability issues of prior models. Our empirical results on the ShapeNet benchmark demonstrate that DiM-3D achieves state-of-the-art performance in generating high-fidelity and diverse 3D shapes. Additionally, DiM-3D shows superior capabilities in tasks like 3D point cloud completion. This not only proves the model's scalability but also underscores its efficiency in generating detailed, high-resolution voxels necessary for advanced 3D shape modeling, particularly excelling in environments requiring high-resolution voxel sizes. Through these findings, we illustrate the exceptional scalability and efficiency of the Diffusion Mamba framework in 3D shape generation, setting a new standard for the field and paving the way for future explorations in high-resolution 3D modeling technologies. | [
"['Shentong Mo']"
] |
null | null | 2406.05041 | null | null | http://arxiv.org/pdf/2406.05041v1 | 2024-06-07T16:14:51Z | 2024-06-07T16:14:51Z | Online Frequency Scheduling by Learning Parallel Actions | Radio Resource Management is a challenging topic in future 6G networks where novel applications create strong competition among the users for the available resources. In this work we consider the frequency scheduling problem in a multi-user MIMO system. Frequency resources need to be assigned to a set of users while allowing for concurrent transmissions in the same sub-band. Traditional methods are insufficient to cope with all the involved constraints and uncertainties, whereas reinforcement learning can directly learn near-optimal solutions for such complex environments. However, the scheduling problem has an enormous action space accounting for all the combinations of users and sub-bands, so out-of-the-box algorithms cannot be used directly. In this work, we propose a scheduler based on action-branching over sub-bands, which is a deep Q-learning architecture with parallel decision capabilities. The sub-bands learn correlated but local decision policies and altogether they optimize a global reward. To improve the scaling of the architecture with the number of sub-bands, we propose variations (Unibranch, Graph Neural Network-based) that reduce the number of parameters to learn. The parallel decision making of the proposed architecture allows to meet short inference time requirements in real systems. Furthermore, the deep Q-learning approach permits online fine-tuning after deployment to bridge the sim-to-real gap. The proposed architectures are evaluated against relevant baselines from the literature showing competitive performance and possibilities of online adaptation to evolving environments. | [
"['Anastasios Giovanidis' 'Mathieu Leconte' 'Sabrine Aroua' 'Tor Kvernvik'\n 'David Sandberg']"
] |
null | null | 2406.05045 | null | null | http://arxiv.org/pdf/2406.05045v1 | 2024-06-07T16:18:32Z | 2024-06-07T16:18:32Z | A Tensor Decomposition Perspective on Second-order RNNs | Second-order Recurrent Neural Networks (2RNNs) extend RNNs by leveraging second-order interactions for sequence modelling. These models are provably more expressive than their first-order counterparts and have connections to well-studied models from formal language theory. However, their large parameter tensor makes computations intractable. To circumvent this issue, one approach known as MIRNN consists in limiting the type of interactions used by the model. Another is to leverage tensor decomposition to diminish the parameter count. In this work, we study the model resulting from parameterizing 2RNNs using the CP decomposition, which we call CPRNN. Intuitively, the rank of the decomposition should reduce expressivity. We analyze how rank and hidden size affect model capacity and show the relationships between RNNs, 2RNNs, MIRNNs, and CPRNNs based on these parameters. We support these results empirically with experiments on the Penn Treebank dataset which demonstrate that, with a fixed parameter budget, CPRNNs outperforms RNNs, 2RNNs, and MIRNNs with the right choice of rank and hidden size. | [
"['Maude Lizaire' 'Michael Rizvi-Martel' 'Marawan Gamal Abdel Hameed'\n 'Guillaume Rabusseau']"
] |
null | null | 2406.05053 | null | null | http://arxiv.org/pdf/2406.05053v1 | 2024-06-07T16:22:51Z | 2024-06-07T16:22:51Z | Hints-In-Browser: Benchmarking Language Models for Programming Feedback
Generation | Generative AI and large language models hold great promise in enhancing programming education by generating individualized feedback and hints for learners. Recent works have primarily focused on improving the quality of generated feedback to achieve human tutors' quality. While quality is an important performance criterion, it is not the only criterion to optimize for real-world educational deployments. In this paper, we benchmark language models for programming feedback generation across several performance criteria, including quality, cost, time, and data privacy. The key idea is to leverage recent advances in the new paradigm of in-browser inference that allow running these models directly in the browser, thereby providing direct benefits across cost and data privacy. To boost the feedback quality of small models compatible with in-browser inference engines, we develop a fine-tuning pipeline based on GPT-4 generated synthetic data. We showcase the efficacy of fine-tuned Llama3-8B and Phi3-3.8B 4-bit quantized models using WebLLM's in-browser inference engine on three different Python programming datasets. We will release the full implementation along with a web app and datasets to facilitate further research on in-browser language models. | [
"['Nachiket Kotalwar' 'Alkis Gotovos' 'Adish Singla']"
] |
null | null | 2406.05061 | null | null | http://arxiv.org/pdf/2406.05061v1 | 2024-06-07T16:33:08Z | 2024-06-07T16:33:08Z | Progressive Entropic Optimal Transport Solvers | Optimal transport (OT) has profoundly impacted machine learning by providing theoretical and computational tools to realign datasets. In this context, given two large point clouds of sizes $n$ and $m$ in $mathbb{R}^d$, entropic OT (EOT) solvers have emerged as the most reliable tool to either solve the Kantorovich problem and output a $ntimes m$ coupling matrix, or to solve the Monge problem and learn a vector-valued push-forward map. While the robustness of EOT couplings/maps makes them a go-to choice in practical applications, EOT solvers remain difficult to tune because of a small but influential set of hyperparameters, notably the omnipresent entropic regularization strength $varepsilon$. Setting $varepsilon$ can be difficult, as it simultaneously impacts various performance metrics, such as compute speed, statistical performance, generalization, and bias. In this work, we propose a new class of EOT solvers (ProgOT), that can estimate both plans and transport maps. We take advantage of several opportunities to optimize the computation of EOT solutions by dividing mass displacement using a time discretization, borrowing inspiration from dynamic OT formulations, and conquering each of these steps using EOT with properly scheduled parameters. We provide experimental evidence demonstrating that ProgOT is a faster and more robust alternative to standard solvers when computing couplings at large scales, even outperforming neural network-based approaches. We also prove statistical consistency of our approach for estimating optimal transport maps. | [
"['Parnian Kassraie' 'Aram-Alexandre Pooladian' 'Michal Klein'\n 'James Thornton' 'Jonathan Niles-Weed' 'Marco Cuturi']"
] |
null | null | 2406.05064 | null | null | http://arxiv.org/pdf/2406.05064v1 | 2024-06-07T16:34:31Z | 2024-06-07T16:34:31Z | Pretraining Decision Transformers with Reward Prediction for In-Context
Multi-task Structured Bandit Learning | In this paper, we study multi-task structured bandit problem where the goal is to learn a near-optimal algorithm that minimizes cumulative regret. The tasks share a common structure and the algorithm exploits the shared structure to minimize the cumulative regret for an unseen but related test task. We use a transformer as a decision-making algorithm to learn this shared structure so as to generalize to the test task. The prior work of pretrained decision transformers like DPT requires access to the optimal action during training which may be hard in several scenarios. Diverging from these works, our learning algorithm does not need the knowledge of optimal action per task during training but predicts a reward vector for each of the actions using only the observed offline data from the diverse training tasks. Finally, during inference time, it selects action using the reward predictions employing various exploration strategies in-context for an unseen test task. Our model outperforms other SOTA methods like DPT, and Algorithmic Distillation over a series of experiments on several structured bandit problems (linear, bilinear, latent, non-linear). Interestingly, we show that our algorithm, without the knowledge of the underlying problem structure, can learn a near-optimal policy in-context by leveraging the shared structure across diverse tasks. We further extend the field of pre-trained decision transformers by showing that they can leverage unseen tasks with new actions and still learn the underlying latent structure to derive a near-optimal policy. We validate this over several experiments to show that our proposed solution is very general and has wide applications to potentially emergent online and offline strategies at test time. Finally, we theoretically analyze the performance of our algorithm and obtain generalization bounds in the in-context multi-task learning setting. | [
"['Subhojyoti Mukherjee' 'Josiah P. Hanna' 'Qiaomin Xie' 'Robert Nowak']"
] |
null | null | 2406.05071 | null | null | http://arxiv.org/pdf/2406.05071v1 | 2024-06-07T16:41:05Z | 2024-06-07T16:41:05Z | Massively Multiagent Minigames for Training Generalist Agents | We present Meta MMO, a collection of many-agent minigames for use as a reinforcement learning benchmark. Meta MMO is built on top of Neural MMO, a massively multiagent environment that has been the subject of two previous NeurIPS competitions. Our work expands Neural MMO with several computationally efficient minigames. We explore generalization across Meta MMO by learning to play several minigames with a single set of weights. We release the environment, baselines, and training code under the MIT license. We hope that Meta MMO will spur additional progress on Neural MMO and, more generally, will serve as a useful benchmark for many-agent generalization. | [
"['Kyoung Whan Choe' 'Ryan Sullivan' 'Joseph Suárez']"
] |
null | null | 2406.05072 | null | null | http://arxiv.org/pdf/2406.05072v1 | 2024-06-07T16:43:54Z | 2024-06-07T16:43:54Z | Linearization Turns Neural Operators into Function-Valued Gaussian
Processes | Modeling dynamical systems, e.g. in climate and engineering sciences, often necessitates solving partial differential equations. Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data. As for all statistical models, the predictions of these models are imperfect and exhibit errors. Such errors are particularly difficult to spot in the complex nonlinear behaviour of dynamical systems. We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes. Our approach can be interpreted as a probabilistic analogue of the concept of currying from functional programming and provides a practical yet theoretically sound way to apply the linearized Laplace approximation to neural operators. In a case study on Fourier neural operators, we show that, even for a discretized input, our method yields a Gaussian closure--a structured Gaussian process posterior capturing the uncertainty in the output function of the neural operator, which can be evaluated at an arbitrary set of points. The method adds minimal prediction overhead, can be applied post-hoc without retraining the neural operator, and scales to large models and datasets. We showcase the efficacy of our approach through applications to different types of partial differential equations. | [
"['Emilia Magnani' 'Marvin Pförtner' 'Tobias Weber' 'Philipp Hennig']"
] |
null | null | 2406.05079 | null | null | http://arxiv.org/pdf/2406.05079v1 | 2024-06-07T16:49:21Z | 2024-06-07T16:49:21Z | SUMIE: A Synthetic Benchmark for Incremental Entity Summarization | No existing dataset adequately tests how well language models can incrementally update entity summaries - a crucial ability as these models rapidly advance. The Incremental Entity Summarization (IES) task is vital for maintaining accurate, up-to-date knowledge. To address this, we introduce SUMIE, a fully synthetic dataset designed to expose real-world IES challenges. This dataset effectively highlights problems like incorrect entity association and incomplete information presentation. Unlike common synthetic datasets, ours captures the complexity and nuances found in real-world data. We generate informative and diverse attributes, summaries, and unstructured paragraphs in sequence, ensuring high quality. The alignment between generated summaries and paragraphs exceeds 96%, confirming the dataset's quality. Extensive experiments demonstrate the dataset's difficulty - state-of-the-art LLMs struggle to update summaries with an F1 higher than 80.4%. We will open source the benchmark and the evaluation metrics to help the community make progress on IES tasks. | [
"['Eunjeong Hwang' 'Yichao Zhou' 'Beliz Gunel' 'James Bradley Wendt'\n 'Sandeep Tata']"
] |
null | null | 2406.05088 | null | null | http://arxiv.org/pdf/2406.05088v1 | 2024-06-07T17:02:37Z | 2024-06-07T17:02:37Z | Optimizing Time Series Forecasting Architectures: A Hierarchical Neural
Architecture Search Approach | The rapid development of time series forecasting research has brought many deep learning-based modules in this field. However, despite the increasing amount of new forecasting architectures, it is still unclear if we have leveraged the full potential of these existing modules within a properly designed architecture. In this work, we propose a novel hierarchical neural architecture search approach for time series forecasting tasks. With the design of a hierarchical search space, we incorporate many architecture types designed for forecasting tasks and allow for the efficient combination of different forecasting architecture modules. Results on long-term-time-series-forecasting tasks show that our approach can search for lightweight high-performing forecasting architectures across different forecasting tasks. | [
"['Difan Deng' 'Marius Lindauer']"
] |
null | null | 2406.05090 | null | null | http://arxiv.org/pdf/2406.05090v1 | 2024-06-07T17:03:43Z | 2024-06-07T17:03:43Z | Provably Better Explanations with Optimized Aggregation of Feature
Attributions | Using feature attributions for post-hoc explanations is a common practice to understand and verify the predictions of opaque machine learning models. Despite the numerous techniques available, individual methods often produce inconsistent and unstable results, putting their overall reliability into question. In this work, we aim to systematically improve the quality of feature attributions by combining multiple explanations across distinct methods or their variations. For this purpose, we propose a novel approach to derive optimal convex combinations of feature attributions that yield provable improvements of desired quality criteria such as robustness or faithfulness to the model behavior. Through extensive experiments involving various model architectures and popular feature attribution techniques, we demonstrate that our combination strategy consistently outperforms individual methods and existing baselines. | [
"['Thomas Decker' 'Ananta R. Bhattarai' 'Jindong Gu' 'Volker Tresp'\n 'Florian Buettner']"
] |
null | null | 2406.05108 | null | null | http://arxiv.org/pdf/2406.05108v1 | 2024-06-07T17:40:38Z | 2024-06-07T17:40:38Z | Adapting Physics-Informed Neural Networks To Optimize ODEs in Mosquito
Population Dynamics | Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modeling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling. In this research, we propose a PINN framework with several improvements for forward and inverse problems for ODE systems with a case study application in modelling the dynamics of mosquito populations. The framework tackles the gradient imbalance and stiff problems posed by mosquito ordinary differential equations. The method offers a simple but effective way to resolve the time causality issue in PINNs by gradually expanding the training time domain until it covers entire domain of interest. As part of a robust evaluation, we conduct experiments using simulated data to evaluate the effectiveness of the approach. Preliminary results indicate that physics-informed machine learning holds significant potential for advancing the study of ecological systems. | [
"['Dinh Viet Cuong' 'Branislava Lalić' 'Mina Petrić' 'Binh Nguyen'\n 'Mark Roantree']"
] |
null | null | 2406.05109 | null | null | http://arxiv.org/pdf/2406.05109v1 | 2024-06-07T17:41:47Z | 2024-06-07T17:41:47Z | Large Generative Graph Models | Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous domains. This training paradigm over diverse well-curated data lies at the heart of generating creative and sensible content. However, all previous graph generative models (e.g., GraphRNN, MDVAE, MoFlow, GDSS, and DiGress) have been trained only on one dataset each time, which cannot replicate the revolutionary success achieved by LGMs in other fields. To remedy this crucial gap, we propose a new class of graph generative model called Large Graph Generative Model (LGGM) that is trained on a large corpus of graphs (over 5000 graphs) from 13 different domains. We empirically demonstrate that the pre-trained LGGM has superior zero-shot generative capability to existing graph generative models. Furthermore, our pre-trained LGGM can be easily fine-tuned with graphs from target domains and demonstrate even better performance than those directly trained from scratch, behaving as a solid starting point for real-world customization. Inspired by Stable Diffusion, we further equip LGGM with the capability to generate graphs given text prompts (Text-to-Graph), such as the description of the network name and domain (i.e., "The power-1138-bus graph represents a network of buses in a power distribution system."), and network statistics (i.e., "The graph has a low average degree, suitable for modeling social media interactions."). This Text-to-Graph capability integrates the extensive world knowledge in the underlying language model, offering users fine-grained control of the generated graphs. We release the code, the model checkpoint, and the datasets at https://lggm-lg.github.io/. | [
"['Yu Wang' 'Ryan A. Rossi' 'Namyong Park' 'Huiyuan Chen'\n 'Nesreen K. Ahmed' 'Puja Trivedi' 'Franck Dernoncourt' 'Danai Koutra'\n 'Tyler Derr']"
] |
null | null | 2406.05113 | null | null | http://arxiv.org/pdf/2406.05113v1 | 2024-06-07T17:44:32Z | 2024-06-07T17:44:32Z | LLavaGuard: VLM-based Safeguards for Vision Dataset Curation and Safety
Assessment | We introduce LlavaGuard, a family of VLM-based safeguard models, offering a versatile framework for evaluating the safety compliance of visual content. Specifically, we designed LlavaGuard for dataset annotation and generative model safeguarding. To this end, we collected and annotated a high-quality visual dataset incorporating a broad safety taxonomy, which we use to tune VLMs on context-aware safety risks. As a key innovation, LlavaGuard's new responses contain comprehensive information, including a safety rating, the violated safety categories, and an in-depth rationale. Further, our introduced customizable taxonomy categories enable the context-specific alignment of LlavaGuard to various scenarios. Our experiments highlight the capabilities of LlavaGuard in complex and real-world applications. We provide checkpoints ranging from 7B to 34B parameters demonstrating state-of-the-art performance, with even the smallest models outperforming baselines like GPT-4. We make our dataset and model weights publicly available and invite further research to address the diverse needs of communities and contexts. | [
"['Lukas Helff' 'Felix Friedrich' 'Manuel Brack' 'Kristian Kersting'\n 'Patrick Schramowski']"
] |
null | null | 2406.05114 | null | null | http://arxiv.org/pdf/2406.05114v1 | 2024-06-07T17:44:48Z | 2024-06-07T17:44:48Z | The Expanding Scope of the Stability Gap: Unveiling its Presence in
Joint Incremental Learning of Homogeneous Tasks | Recent research identified a temporary performance drop on previously learned tasks when transitioning to a new one. This drop is called the stability gap and has great consequences for continual learning: it complicates the direct employment of continually learning since the worse-case performance at task-boundaries is dramatic, it limits its potential as an energy-efficient training paradigm, and finally, the stability drop could result in a reduced final performance of the algorithm. In this paper, we show that the stability gap also occurs when applying joint incremental training of homogeneous tasks. In this scenario, the learner continues training on the same data distribution and has access to all data from previous tasks. In addition, we show that in this scenario, there exists a low-loss linear path to the next minima, but that SGD optimization does not choose this path. We perform further analysis including a finer batch-wise analysis which could provide insights towards potential solution directions. | [
"['Sandesh Kamath' 'Albin Soutif-Cormerais' 'Joost van de Weijer'\n 'Bogdan Raducanu']"
] |
null | null | 2406.05119 | null | null | http://arxiv.org/pdf/2406.05119v1 | 2024-06-07T17:50:15Z | 2024-06-07T17:50:15Z | Compositional Curvature Bounds for Deep Neural Networks | A key challenge that threatens the widespread use of neural networks in safety-critical applications is their vulnerability to adversarial attacks. In this paper, we study the second-order behavior of continuously differentiable deep neural networks, focusing on robustness against adversarial perturbations. First, we provide a theoretical analysis of robustness and attack certificates for deep classifiers by leveraging local gradients and upper bounds on the second derivative (curvature constant). Next, we introduce a novel algorithm to analytically compute provable upper bounds on the second derivative of neural networks. This algorithm leverages the compositional structure of the model to propagate the curvature bound layer-by-layer, giving rise to a scalable and modular approach. The proposed bound can serve as a differentiable regularizer to control the curvature of neural networks during training, thereby enhancing robustness. Finally, we demonstrate the efficacy of our method on classification tasks using the MNIST and CIFAR-10 datasets. | [
"['Taha Entesari' 'Sina Sharifi' 'Mahyar Fazlyab']"
] |
null | null | 2406.05132 | null | null | http://arxiv.org/pdf/2406.05132v2 | 2024-06-12T17:59:58Z | 2024-06-07T17:59:59Z | 3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and
Less Hallucination | The integration of language and 3D perception is crucial for developing embodied agents and robots that comprehend and interact with the physical world. While large language models (LLMs) have demonstrated impressive language understanding and generation capabilities, their adaptation to 3D environments (3D-LLMs) remains in its early stages. A primary challenge is the absence of large-scale datasets that provide dense grounding between language and 3D scenes. In this paper, we introduce 3D-GRAND, a pioneering large-scale dataset comprising 40,087 household scenes paired with 6.2 million densely-grounded scene-language instructions. Our results show that instruction tuning with 3D-GRAND significantly enhances grounding capabilities and reduces hallucinations in 3D-LLMs. As part of our contributions, we propose a comprehensive benchmark 3D-POPE to systematically evaluate hallucination in 3D-LLMs, enabling fair comparisons among future models. Our experiments highlight a scaling effect between dataset size and 3D-LLM performance, emphasizing the critical role of large-scale 3D-text datasets in advancing embodied AI research. Notably, our results demonstrate early signals for effective sim-to-real transfer, indicating that models trained on large synthetic data can perform well on real-world 3D scans. Through 3D-GRAND and 3D-POPE, we aim to equip the embodied AI community with essential resources and insights, setting the stage for more reliable and better-grounded 3D-LLMs. Project website: https://3d-grand.github.io | [
"['Jianing Yang' 'Xuweiyi Chen' 'Nikhil Madaan' 'Madhavan Iyengar'\n 'Shengyi Qian' 'David F. Fouhey' 'Joyce Chai']"
] |
null | null | 2406.05142 | null | null | http://arxiv.org/pdf/2406.05142v1 | 2024-05-28T05:06:37Z | 2024-05-28T05:06:37Z | Machine Learning-Driven Optimization of TPMS Architected Materials Using
Simulated Annealing | The research paper presents a novel approach to optimizing the tensile stress of Triply Periodic Minimal Surface (TPMS) structures through machine learning and Simulated Annealing (SA). The study evaluates the performance of Random Forest, Decision Tree, and XGBoost models in predicting tensile stress, using a dataset generated from finite element analysis of TPMS models. The objective function minimized the negative R-squared value on the validation set to enhance model accuracy. The SA-XGBoost model outperformed the others, achieving an R-squared value of 0.96. In contrast, the SA-Random Forest model achieved an R squared value of 0.89 while the SA-Decision Tree model exhibited greater fluctuations in validation scores. This demonstrates that the SA-XGBoost model is most effective in capturing the complex relationships within the data. The integration of SA helps in optimizing the hyperparameters of these machine learning models, thereby enhancing their predictive capabilities. | [
"['Akshansh Mishra']"
] |
null | null | 2406.05143 | null | null | http://arxiv.org/pdf/2406.05143v1 | 2024-05-28T15:41:16Z | 2024-05-28T15:41:16Z | Determining Domain of Machine Learning Models using Kernel Density
Estimates: Applications in Materials Property Prediction | Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions. In this work, we develop a new approach of assessing model domain and demonstrate that our approach provides accurate and meaningful designation of in-domain versus out-of-domain when applied across multiple model types and material property data sets. Our approach assesses the distance between a test and training data point in feature space by using kernel density estimation and shows that this distance provides an effective tool for domain determination. We show that chemical groups considered unrelated based on established chemical knowledge exhibit significant dissimilarities by our measure. We also show that high measures of dissimilarity are associated with poor model performance (i.e., high residual magnitudes) and poor estimates of model uncertainty (i.e., unreliable uncertainty estimation). Automated tools are provided to enable researchers to establish acceptable dissimilarity thresholds to identify whether new predictions of their own machine learning models are in-domain versus out-of-domain. | [
"['Lane E. Schultz' 'Yiqi Wang' 'Ryan Jacobs' 'Dane Morgan']"
] |
null | null | 2406.05149 | null | null | http://arxiv.org/pdf/2406.05149v1 | 2024-06-01T20:06:48Z | 2024-06-01T20:06:48Z | Effective Data Selection for Seismic Interpretation through Disagreement | This paper presents a discussion on data selection for deep learning in the field of seismic interpretation. In order to achieve a robust generalization to the target volume, it is crucial to identify the specific samples are the most informative to the training process. The selection of the training set from a target volume is a critical factor in determining the effectiveness of the deep learning algorithm for interpreting seismic volumes. This paper proposes the inclusion of interpretation disagreement as a valuable and intuitive factor in the process of selecting training sets. The development of a novel data selection framework is inspired by established practices in seismic interpretation. The framework we have developed utilizes representation shifts to effectively model interpretation disagreement within neural networks. Additionally, it incorporates the disagreement measure to enhance attention towards geologically interesting regions throughout the data selection workflow. By combining this approach with active learning, a well-known machine learning paradigm for data selection, we arrive at a comprehensive and innovative framework for training set selection in seismic interpretation. In addition, we offer a specific implementation of our proposed framework, which we have named ATLAS. This implementation serves as a means for data selection. In this study, we present the results of our comprehensive experiments, which clearly indicate that ATLAS consistently surpasses traditional active learning frameworks in the field of seismic interpretation. Our findings reveal that ATLAS achieves improvements of up to 12% in mean intersection-over-union. | [
"['Ryan Benkert' 'Mohit Prabhushankar' 'Ghassan AlRegib']"
] |
null | null | 2406.05152 | null | null | http://arxiv.org/pdf/2406.05152v1 | 2024-06-04T08:18:05Z | 2024-06-04T08:18:05Z | Fight Scene Detection for Movie Highlight Generation System | In this paper of a research based project, using Bidirectional Long Short-Term Memory (BiLSTM) networks, we provide a novel Fight Scene Detection (FSD) model which can be used for Movie Highlight Generation Systems (MHGS) based on deep learning and Neural Networks . Movies usually have Fight Scenes to keep the audience amazed. For trailer generation, or any other application of Highlight generation, it is very tidious to first identify all such scenes manually and then compile them to generate a highlight serving the purpose. Our proposed FSD system utilises temporal characteristics of the movie scenes and thus is capable to automatically identify fight scenes. Thereby helping in the effective production of captivating movie highlights. We observe that the proposed solution features 93.5% accuracy and is higher than 2D CNN with Hough Forests which being 92% accurate and is significantly higher than 3D CNN which features an accuracy of 65%. | [
"['Aryan Mathur']"
] |
null | null | 2406.05153 | null | null | http://arxiv.org/pdf/2406.05153v1 | 2024-06-04T11:30:40Z | 2024-06-04T11:30:40Z | Elastic Full-Waveform Inversion : How the physics of problem improves
data-driven techniques? | Full-Waveform Inversion (FWI) is a nonlinear iterative seismic imaging technique that, by reducing the misfit between recorded and predicted seismic waveforms, can produce detailed estimates of subsurface geophysical properties. Nevertheless, the strong nonlinearity of FWI can trap the optimization in local minima. This issue arises due to factors such as improper initial values, the absence of low frequencies in the measurements, noise, and other related considerations. To address this challenge and with the advent of advanced machine-learning techniques, data-driven methods, such as deep learning, have attracted significantly increasing attention in the geophysical community. Furthermore, the elastic wave equation should be included in FWI to represent elastic effects accurately. The intersection of data-driven techniques and elastic scattering theories presents opportunities and challenges. In this paper, by using the knowledge of elastic scattering (Physics of problem) and integrating it with deep learning techniques, we propose methods for the solution of time-harmonic FWI to enhance accuracy compared to pure data-driven approaches. Moreover, by modifying the structure of the Variational Autoencoder, we introduce a probabilistic deep learning method based on the physics of the problem that enables us to explore the uncertainties of the solution. According to the limited availability of datasets in this field and to assess the performance and accuracy of the proposed methods, we create a comprehensive dataset close to reality and conduct a comparative analysis of the presented approaches to it. | [
"['Vahid Negahdari' 'Seyed Reza Moghadasi' 'Mohammad Reza Razvan']"
] |
null | null | 2406.05175 | null | null | http://arxiv.org/pdf/2406.05175v1 | 2024-06-07T16:33:23Z | 2024-06-07T16:33:23Z | Robust quantum dots charge autotuning using neural networks uncertainty | This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural networks' uncertainty estimations. Tested across three distinct offline experimental datasets representing different single quantum dot technologies, the approach achieves over 99% tuning success rate in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure. | [
"['Victor Yon' 'Bastien Galaup' 'Claude Rohrbacher' 'Joffrey Rivard'\n 'Clément Godfrin' 'Roy Li' 'Stefan Kubicek' 'Kristiaan De Greve'\n 'Louis Gaudreau' 'Eva Dupont-Ferrier' 'Yann Beilliard' 'Roger G. Melko'\n 'Dominique Drouin']"
] |
null | null | 2406.05183 | null | null | http://arxiv.org/pdf/2406.05183v1 | 2024-06-07T18:00:37Z | 2024-06-07T18:00:37Z | The Factorization Curse: Which Tokens You Predict Underlie the Reversal
Curse and More | Today's best language models still struggle with hallucinations: factually incorrect generations, which impede their ability to reliably retrieve information seen during training. The reversal curse, where models cannot recall information when probed in a different order than was encountered during training, exemplifies this in information retrieval. We reframe the reversal curse as a factorization curse - a failure of models to learn the same joint distribution under different factorizations. Through a series of controlled experiments with increasing levels of realism including WikiReversal, a setting we introduce to closely simulate a knowledge intensive finetuning task, we find that the factorization curse is an inherent failure of the next-token prediction objective used in popular large language models. Moreover, we demonstrate reliable information retrieval cannot be solved with scale, reversed tokens, or even naive bidirectional-attention training. Consequently, various approaches to finetuning on specialized data would necessarily provide mixed results on downstream tasks, unless the model has already seen the right sequence of tokens. Across five tasks of varying levels of complexity, our results uncover a promising path forward: factorization-agnostic objectives can significantly mitigate the reversal curse and hint at improved knowledge storage and planning capabilities. | [
"['Ouail Kitouni' 'Niklas Nolte' 'Diane Bouchacourt' 'Adina Williams'\n 'Mike Rabbat' 'Mark Ibrahim']"
] |
null | null | 2406.05187 | null | null | http://arxiv.org/pdf/2406.05187v1 | 2024-06-07T18:12:04Z | 2024-06-07T18:12:04Z | How to Strategize Human Content Creation in the Era of GenAI? | Generative AI (GenAI) will have significant impact on content creation platforms. In this paper, we study the dynamic competition between a GenAI and a human contributor. Unlike the human, the GenAI's content only improves when more contents are created by human over the time; however, GenAI has the advantage of generating content at a lower cost. We study the algorithmic problem in this dynamic competition model about how the human contributor can maximize her utility when competing against the GenAI for content generation over a set of topics. In time-sensitive content domains (e.g., news or pop music creation) where contents' value diminishes over time, we show that there is no polynomial time algorithm for finding the human's optimal (dynamic) strategy, unless the randomized exponential time hypothesis is false. Fortunately, we are able to design a polynomial time algorithm that naturally cycles between myopically optimizing over a short time window and pausing and provably guarantees an approximation ratio of $frac{1}{2}$. We then turn to time-insensitive content domains where contents do not lose their value (e.g., contents on history facts). Interestingly, we show that this setting permits a polynomial time algorithm that maximizes the human's utility in the long run. | [
"['Seyed A. Esmaeili' 'Kshipra Bhawalkar' 'Zhe Feng' 'Di Wang' 'Haifeng Xu']"
] |
null | null | 2406.05190 | null | null | http://arxiv.org/pdf/2406.05190v1 | 2024-06-07T18:13:27Z | 2024-06-07T18:13:27Z | Evaluating the Effectiveness of Data Augmentation for Emotion
Classification in Low-Resource Settings | Data augmentation has the potential to improve the performance of machine learning models by increasing the amount of training data available. In this study, we evaluated the effectiveness of different data augmentation techniques for a multi-label emotion classification task using a low-resource dataset. Our results showed that Back Translation outperformed autoencoder-based approaches and that generating multiple examples per training instance led to further performance improvement. In addition, we found that Back Translation generated the most diverse set of unigrams and trigrams. These findings demonstrate the utility of Back Translation in enhancing the performance of emotion classification models in resource-limited situations. | [
"['Aashish Arora' 'Elsbeth Turcan']"
] |
null | null | 2406.05194 | null | null | http://arxiv.org/pdf/2406.05194v1 | 2024-06-07T18:21:26Z | 2024-06-07T18:21:26Z | LLMs Are Not Intelligent Thinkers: Introducing Mathematical Topic Tree
Benchmark for Comprehensive Evaluation of LLMs | Large language models (LLMs) demonstrate impressive capabilities in mathematical reasoning. However, despite these achievements, current evaluations are mostly limited to specific mathematical topics, and it remains unclear whether LLMs are genuinely engaging in reasoning. To address these gaps, we present the Mathematical Topics Tree (MaTT) benchmark, a challenging and structured benchmark that offers 1,958 questions across a wide array of mathematical subjects, each paired with a detailed hierarchical chain of topics. Upon assessing different LLMs using the MaTT benchmark, we find that the most advanced model, GPT-4, achieved a mere 54% accuracy in a multiple-choice scenario. Interestingly, even when employing Chain-of-Thought prompting, we observe mostly no notable improvement. Moreover, LLMs accuracy dramatically reduced by up to 24.2 percentage point when the questions were presented without providing choices. Further detailed analysis of the LLMs' performance across a range of topics showed significant discrepancy even for closely related subtopics within the same general mathematical area. In an effort to pinpoint the reasons behind LLMs performances, we conducted a manual evaluation of the completeness and correctness of the explanations generated by GPT-4 when choices were available. Surprisingly, we find that in only 53.3% of the instances where the model provided a correct answer, the accompanying explanations were deemed complete and accurate, i.e., the model engaged in genuine reasoning. | [
"['Arash Gholami Davoodi' 'Seyed Pouyan Mousavi Davoudi'\n 'Pouya Pezeshkpour']"
] |
null | null | 2406.05205 | null | null | http://arxiv.org/pdf/2406.05205v1 | 2024-06-07T18:39:58Z | 2024-06-07T18:39:58Z | CPLIP: Zero-Shot Learning for Histopathology with Comprehensive
Vision-Language Alignment | This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology enriches vision-language models by leveraging extensive data without needing ground truth annotations. CPLIP involves constructing a pathology-specific dictionary, generating textual descriptions for images using language models, and retrieving relevant images for each text snippet via a pre-trained model. The model is then fine-tuned using a many-to-many contrastive learning method to align complex interrelated concepts across both modalities. Evaluated across multiple histopathology tasks, CPLIP shows notable improvements in zero-shot learning scenarios, outperforming existing methods in both interpretability and robustness and setting a higher benchmark for the application of vision-language models in the field. To encourage further research and replication, the code for CPLIP is available on GitHub at https://cplip.github.io/ | [
"['Sajid Javed' 'Arif Mahmood' 'Iyyakutti Iyappan Ganapathi'\n 'Fayaz Ali Dharejo' 'Naoufel Werghi' 'Mohammed Bennamoun']"
] |
null | null | 2406.05207 | null | null | http://arxiv.org/pdf/2406.05207v1 | 2024-06-07T18:43:33Z | 2024-06-07T18:43:33Z | Retrieval & Fine-Tuning for In-Context Tabular Models | Tabular data is a pervasive modality spanning a wide range of domains, and the inherent diversity poses a considerable challenge for deep learning. Recent advancements using transformer-based in-context learning have shown promise on smaller and less complex datasets, but have struggled to scale to larger and more complex ones. To address this limitation, we propose a combination of retrieval and fine-tuning: we can adapt the transformer to a local subset of the data by collecting nearest neighbours, and then perform task-specific fine-tuning with this retrieved set of neighbours in context. Using TabPFN as the base model -- currently the best tabular in-context learner -- and applying our retrieval and fine-tuning scheme on top results in what we call a locally-calibrated PFN, or LoCalPFN. We conduct extensive evaluation on 95 datasets curated by TabZilla from OpenML, upon which we establish a new state-of-the-art with LoCalPFN -- even with respect to tuned tree-based models. Notably, we show a significant boost in performance compared to the base in-context model, demonstrating the efficacy of our approach and advancing the frontier of deep learning in tabular data. | [
"['Valentin Thomas' 'Junwei Ma' 'Rasa Hosseinzadeh' 'Keyvan Golestan'\n 'Guangwei Yu' 'Maksims Volkovs' 'Anthony Caterini']"
] |
null | null | 2406.05213 | null | null | http://arxiv.org/pdf/2406.05213v1 | 2024-06-07T18:54:40Z | 2024-06-07T18:54:40Z | On Subjective Uncertainty Quantification and Calibration in Natural
Language Generation | Applications of large language models often involve the generation of free-form responses, in which case uncertainty quantification becomes challenging. This is due to the need to identify task-specific uncertainties (e.g., about the semantics) which appears difficult to define in general cases. This work addresses these challenges from a perspective of Bayesian decision theory, starting from the assumption that our utility is characterized by a similarity measure that compares a generated response with a hypothetical true response. We discuss how this assumption enables principled quantification of the model's subjective uncertainty and its calibration. We further derive a measure for epistemic uncertainty, based on a missing data perspective and its characterization as an excess risk. The proposed measures can be applied to black-box language models. We demonstrate the proposed methods on question answering and machine translation tasks, where they extract broadly meaningful uncertainty estimates from GPT and Gemini models and quantify their calibration. | [
"['Ziyu Wang' 'Chris Holmes']"
] |
null | null | 2406.05216 | null | null | http://arxiv.org/pdf/2406.05216v1 | 2024-06-07T18:59:37Z | 2024-06-07T18:59:37Z | TabPFGen -- Tabular Data Generation with TabPFN | Advances in deep generative modelling have not translated well to tabular data. We argue that this is caused by a mismatch in structure between popular generative models and discriminative models of tabular data. We thus devise a technique to turn TabPFN -- a highly performant transformer initially designed for in-context discriminative tabular tasks -- into an energy-based generative model, which we dub TabPFGen. This novel framework leverages the pre-trained TabPFN as part of the energy function and does not require any additional training or hyperparameter tuning, thus inheriting TabPFN's in-context learning capability. We can sample from TabPFGen analogously to other energy-based models. We demonstrate strong results on standard generative modelling tasks, including data augmentation, class-balancing, and imputation, unlocking a new frontier of tabular data generation. | [
"['Junwei Ma' 'Apoorv Dankar' 'George Stein' 'Guangwei Yu'\n 'Anthony Caterini']"
] |
null | null | 2406.05222 | null | null | http://arxiv.org/pdf/2406.05222v1 | 2024-06-07T19:10:31Z | 2024-06-07T19:10:31Z | Towards Interpretable Deep Local Learning with Successive Gradient
Reconciliation | Relieving the reliance of neural network training on a global back-propagation (BP) has emerged as a notable research topic due to the biological implausibility and huge memory consumption caused by BP. Among the existing solutions, local learning optimizes gradient-isolated modules of a neural network with local errors and has been proved to be effective even on large-scale datasets. However, the reconciliation among local errors has never been investigated. In this paper, we first theoretically study non-greedy layer-wise training and show that the convergence cannot be assured when the local gradient in a module w.r.t. its input is not reconciled with the local gradient in the previous module w.r.t. its output. Inspired by the theoretical result, we further propose a local training strategy that successively regularizes the gradient reconciliation between neighboring modules without breaking gradient isolation or introducing any learnable parameters. Our method can be integrated into both local-BP and BP-free settings. In experiments, we achieve significant performance improvements compared to previous methods. Particularly, our method for CNN and Transformer architectures on ImageNet is able to attain a competitive performance with global BP, saving more than 40% memory consumption. | [
"['Yibo Yang' 'Xiaojie Li' 'Motasem Alfarra' 'Hasan Hammoud' 'Adel Bibi'\n 'Philip Torr' 'Bernard Ghanem']"
] |
null | null | 2406.05223 | null | null | http://arxiv.org/pdf/2406.05223v1 | 2024-06-07T19:10:35Z | 2024-06-07T19:10:35Z | CorDA: Context-Oriented Decomposition Adaptation of Large Language
Models | Current parameter-efficient fine-tuning (PEFT) methods build adapters without considering the context of downstream task to learn, or the context of important knowledge to maintain. As a result, there is often a performance gap compared to full-parameter finetuning, and meanwhile the finetuned model suffers from catastrophic forgetting of the pre-trained world knowledge. In this paper, we propose CorDA, a Context-oriented Decomposition Adaptation method that builds learnable adapters from weight decomposition oriented by the context of downstream task or world knowledge. Concretely, we collect a few data samples, and perform singular value decomposition for each linear layer of a pre-trained LLM multiplied by the covariance matrix of the input activation using these samples. By doing so, the context of the representative samples is captured through deciding the factorizing orientation. Our method enables two options, the knowledge-preserved adaptation and the instruction-previewed adaptation. For the former, we use question-answering samples to obtain the covariance matrices, and use the decomposed components with the smallest $r$ singular values to initialize a learnable adapter, with the others frozen such that the world knowledge is better preserved. For the latter, we use the instruction data from the finetuning task, such as math or coding, to orientate the decomposition and train the largest $r$ components that capture the main characteristics of the task to learn. We conduct extensive experiments on Math, Code, and Instruction Following tasks. Our knowledge-preserved adaptation not only achieves better performance than LoRA on finetuning tasks, but also mitigates the forgetting of world knowledge. Our instruction-previewed adaptation is able to further enhance the finetuning performance, surpassing full-parameter finetuning and the state-of-the-art PEFT methods. | [
"['Yibo Yang' 'Xiaojie Li' 'Zhongzhu Zhou' 'Shuaiwen Leon Song'\n 'Jianlong Wu' 'Liqiang Nie' 'Bernard Ghanem']"
] |
null | null | 2406.05225 | null | null | http://arxiv.org/pdf/2406.05225v1 | 2024-06-07T19:25:02Z | 2024-06-07T19:25:02Z | A Manifold Perspective on the Statistical Generalization of Graph Neural
Networks | Convolutional neural networks have been successfully extended to operate on graphs, giving rise to Graph Neural Networks (GNNs). GNNs combine information from adjacent nodes by successive applications of graph convolutions. GNNs have been implemented successfully in various learning tasks while the theoretical understanding of their generalization capability is still in progress. In this paper, we leverage manifold theory to analyze the statistical generalization gap of GNNs operating on graphs constructed on sampled points from manifolds. We study the generalization gaps of GNNs on both node-level and graph-level tasks. We show that the generalization gaps decrease with the number of nodes in the training graphs, which guarantees the generalization of GNNs to unseen points over manifolds. We validate our theoretical results in multiple real-world datasets. | [
"['Zhiyang Wang' 'Juan Cervino' 'Alejandro Ribeiro']"
] |
null | null | 2406.05227 | null | null | http://arxiv.org/pdf/2406.05227v1 | 2024-06-07T19:29:55Z | 2024-06-07T19:29:55Z | Mixed-Curvature Decision Trees and Random Forests | We extend decision tree and random forest algorithms to mixed-curvature product spaces. Such spaces, defined as Cartesian products of Euclidean, hyperspherical, and hyperbolic manifolds, can often embed points from pairwise distances with much lower distortion than in single manifolds. To date, all classifiers for product spaces fit a single linear decision boundary, and no regressor has been described. Our method overcomes these limitations by enabling simple, expressive classification and regression in product manifolds. We demonstrate the superior accuracy of our tool compared to Euclidean methods operating in the ambient space for component manifolds covering a wide range of curvatures, as well as on a selection of product manifolds. | [
"['Philippe Chlenski' 'Quentin Chu' \"Itsik Pe'er\"]"
] |
null | null | 2406.05231 | null | null | http://arxiv.org/pdf/2406.05231v2 | 2024-06-21T09:23:17Z | 2024-06-07T19:37:59Z | The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D
Universal Lesion Segmentation in Computed Tomography | Size measurements of tumor manifestations on follow-up CT examinations are crucial for evaluating treatment outcomes in cancer patients. Efficient lesion segmentation can speed up these radiological workflows. While numerous benchmarks and challenges address lesion segmentation in specific organs like the liver, kidneys, and lungs, the larger variety of lesion types encountered in clinical practice demands a more universal approach. To address this gap, we introduced the ULS23 benchmark for 3D universal lesion segmentation in chest-abdomen-pelvis CT examinations. The ULS23 training dataset contains 38,693 lesions across this region, including challenging pancreatic, colon and bone lesions. For evaluation purposes, we curated a dataset comprising 775 lesions from 284 patients. Each of these lesions was identified as a target lesion in a clinical context, ensuring diversity and clinical relevance within this dataset. The ULS23 benchmark is publicly accessible via uls23.grand-challenge.org, enabling researchers worldwide to assess the performance of their segmentation methods. Furthermore, we have developed and publicly released our baseline semi-supervised 3D lesion segmentation model. This model achieved an average Dice coefficient of 0.703 $pm$ 0.240 on the challenge test set. We invite ongoing submissions to advance the development of future ULS models. | [
"['M. J. J. de Grauw' 'E. Th. Scholten' 'E. J. Smit' 'M. J. C. M. Rutten'\n 'M. Prokop' 'B. van Ginneken' 'A. Hering']"
] |
null | null | 2406.05232 | null | null | http://arxiv.org/pdf/2406.05232v2 | 2024-06-11T16:41:52Z | 2024-06-07T19:38:05Z | Improving Logits-based Detector without Logits from Black-box LLMs | The advent of Large Language Models (LLMs) has revolutionized text generation, producing outputs that closely mimic human writing. This blurring of lines between machine- and human-written text presents new challenges in distinguishing one from the other a task further complicated by the frequent updates and closed nature of leading proprietary LLMs. Traditional logits-based detection methods leverage surrogate models for identifying LLM-generated content when the exact logits are unavailable from black-box LLMs. However, these methods grapple with the misalignment between the distributions of the surrogate and the often undisclosed target models, leading to performance degradation, particularly with the introduction of new, closed-source models. Furthermore, while current methodologies are generally effective when the source model is identified, they falter in scenarios where the model version remains unknown, or the test set comprises outputs from various source models. To address these limitations, we present Distribution-Aligned LLMs Detection (DALD), an innovative framework that redefines the state-of-the-art performance in black-box text detection even without logits from source LLMs. DALD is designed to align the surrogate model's distribution with that of unknown target LLMs, ensuring enhanced detection capability and resilience against rapid model iterations with minimal training investment. By leveraging corpus samples from publicly accessible outputs of advanced models such as ChatGPT, GPT-4 and Claude-3, DALD fine-tunes surrogate models to synchronize with unknown source model distributions effectively. | [
"['Cong Zeng' 'Shengkun Tang' 'Xianjun Yang' 'Yuanzhou Chen' 'Yiyou Sun'\n 'zhiqiang xu' 'Yao Li' 'Haifeng Chen' 'Wei Cheng' 'Dongkuan Xu']"
] |
null | null | 2406.05233 | null | null | http://arxiv.org/pdf/2406.05233v1 | 2024-06-07T19:42:05Z | 2024-06-07T19:42:05Z | Federated LoRA with Sparse Communication | Low-rank adaptation (LoRA) is a natural method for finetuning in communication-constrained machine learning settings such as cross-device federated learning. Prior work that has studied LoRA in the context of federated learning has focused on improving LoRA's robustness to heterogeneity and privacy. In this work, we instead consider techniques for further improving communication-efficiency in federated LoRA. Unfortunately, we show that centralized ML methods that improve the efficiency of LoRA through unstructured pruning do not transfer well to federated settings. We instead study a simple approach, textbf{FLASC}, that applies sparsity to LoRA during communication while allowing clients to locally fine-tune the entire LoRA module. Across four common federated learning tasks, we demonstrate that this method matches the performance of dense LoRA with up to $10times$ less communication. Additionally, despite being designed primarily to target communication, we find that this approach has benefits in terms of heterogeneity and privacy relative to existing approaches tailored to these specific concerns. Overall, our work highlights the importance of considering system-specific constraints when developing communication-efficient finetuning approaches, and serves as a simple and competitive baseline for future work in federated finetuning. | [
"['Kevin Kuo' 'Arian Raje' 'Kousik Rajesh' 'Virginia Smith']"
] |
null | null | 2406.05250 | null | null | http://arxiv.org/pdf/2406.05250v2 | 2024-06-19T20:49:26Z | 2024-06-07T20:22:36Z | LLM-Enhanced Bayesian Optimization for Efficient Analog Layout
Constraint Generation | Analog layout synthesis faces significant challenges due to its dependence on manual processes, considerable time requirements, and performance instability. Current Bayesian Optimization (BO)-based techniques for analog layout synthesis, despite their potential for automation, suffer from slow convergence and extensive data needs, limiting their practical application. This paper presents the texttt{LLANA} framework, a novel approach that leverages Large Language Models (LLMs) to enhance BO by exploiting the few-shot learning abilities of LLMs for more efficient generation of analog design-dependent parameter constraints. Experimental results demonstrate that texttt{LLANA} not only achieves performance comparable to state-of-the-art (SOTA) BO methods but also enables a more effective exploration of the analog circuit design space, thanks to LLM's superior contextual understanding and learning efficiency. The code is available at https://github.com/dekura/LLANA. | [
"['Guojin Chen' 'Keren Zhu' 'Seunggeun Kim' 'Hanqing Zhu' 'Yao Lai'\n 'Bei Yu' 'David Z. Pan']"
] |
null | null | 2406.05251 | null | null | http://arxiv.org/pdf/2406.05251v1 | 2024-06-07T20:25:05Z | 2024-06-07T20:25:05Z | Automated Trustworthiness Testing for Machine Learning Classifiers | Machine Learning (ML) has become an integral part of our society, commonly used in critical domains such as finance, healthcare, and transportation. Therefore, it is crucial to evaluate not only whether ML models make correct predictions but also whether they do so for the correct reasons, ensuring our trust that will perform well on unseen data. This concept is known as trustworthiness in ML. Recently, explainable techniques (e.g., LIME, SHAP) have been developed to interpret the decision-making processes of ML models, providing explanations for their predictions (e.g., words in the input that influenced the prediction the most). Assessing the plausibility of these explanations can enhance our confidence in the models' trustworthiness. However, current approaches typically rely on human judgment to determine the plausibility of these explanations. This paper proposes TOWER, the first technique to automatically create trustworthiness oracles that determine whether text classifier predictions are trustworthy. It leverages word embeddings to automatically evaluate the trustworthiness of a model-agnostic text classifiers based on the outputs of explanatory techniques. Our hypothesis is that a prediction is trustworthy if the words in its explanation are semantically related to the predicted class. We perform unsupervised learning with untrustworthy models obtained from noisy data to find the optimal configuration of TOWER. We then evaluated TOWER on a human-labeled trustworthiness dataset that we created. The results show that TOWER can detect a decrease in trustworthiness as noise increases, but is not effective when evaluated against the human-labeled dataset. Our initial experiments suggest that our hypothesis is valid and promising, but further research is needed to better understand the relationship between explanations and trustworthiness issues. | [
"['Steven Cho' 'Seaton Cousins-Baxter' 'Stefano Ruberto' 'Valerio Terragni']"
] |
null | null | 2406.05257 | null | null | http://arxiv.org/pdf/2406.05257v1 | 2024-06-07T21:00:20Z | 2024-06-07T21:00:20Z | Efficient Differentially Private Fine-Tuning of Diffusion Models | The recent developments of Diffusion Models (DMs) enable generation of astonishingly high-quality synthetic samples. Recent work showed that the synthetic samples generated by the diffusion model, which is pre-trained on public data and fully fine-tuned with differential privacy on private data, can train a downstream classifier, while achieving a good privacy-utility tradeoff. However, fully fine-tuning such large diffusion models with DP-SGD can be very resource-demanding in terms of memory usage and computation. In this work, we investigate Parameter-Efficient Fine-Tuning (PEFT) of diffusion models using Low-Dimensional Adaptation (LoDA) with Differential Privacy. We evaluate the proposed method with the MNIST and CIFAR-10 datasets and demonstrate that such efficient fine-tuning can also generate useful synthetic samples for training downstream classifiers, with guaranteed privacy protection of fine-tuning data. Our source code will be made available on GitHub. | [
"['Jing Liu' 'Andrew Lowy' 'Toshiaki Koike-Akino' 'Kieran Parsons'\n 'Ye Wang']"
] |
null | null | 2406.05260 | null | null | http://arxiv.org/pdf/2406.05260v1 | 2024-06-07T21:07:35Z | 2024-06-07T21:07:35Z | Generative modeling of density regression through tree flows | A common objective in the analysis of tabular data is estimating the conditional distribution (in contrast to only producing predictions) of a set of "outcome" variables given a set of "covariates", which is sometimes referred to as the "density regression" problem. Beyond estimation on the conditional distribution, the generative ability of drawing synthetic samples from the learned conditional distribution is also desired as it further widens the range of applications. We propose a flow-based generative model tailored for the density regression task on tabular data. Our flow applies a sequence of tree-based piecewise-linear transforms on initial uniform noise to eventually generate samples from complex conditional densities of (univariate or multivariate) outcomes given the covariates and allows efficient analytical evaluation of the fitted conditional density on any point in the sample space. We introduce a training algorithm for fitting the tree-based transforms using a divide-and-conquer strategy that transforms maximum likelihood training of the tree-flow into training a collection of binary classifiers--one at each tree split--under cross-entropy loss. We assess the performance of our method under out-of-sample likelihood evaluation and compare it with a variety of state-of-the-art conditional density learners on a range of simulated and real benchmark tabular datasets. Our method consistently achieves comparable or superior performance at a fraction of the training and sampling budget. Finally, we demonstrate the utility of our method's generative ability through an application to generating synthetic longitudinal microbiome compositional data based on training our flow on a publicly available microbiome study. | [
"['Zhuoqun Wang' 'Naoki Awaya' 'Li Ma']"
] |
null | null | 2406.05270 | null | null | http://arxiv.org/pdf/2406.05270v1 | 2024-06-07T21:37:48Z | 2024-06-07T21:37:48Z | fastMRI Breast: A publicly available radial k-space dataset of breast
dynamic contrast-enhanced MRI | This data curation work introduces the first large-scale dataset of radial k-space and DICOM data for breast DCE-MRI acquired in diagnostic breast MRI exams. Our dataset includes case-level labels indicating patient age, menopause status, lesion status (negative, benign, and malignant), and lesion type for each case. The public availability of this dataset and accompanying reconstruction code will support research and development of fast and quantitative breast image reconstruction and machine learning methods. | [
"['Eddy Solomon' 'Patricia M. Johnson' 'Zhengguo Tan' 'Radhika Tibrewala'\n 'Yvonne W. Lui' 'Florian Knoll' 'Linda Moy' 'Sungheon Gene Kim'\n 'Laura Heacock']"
] |
null | null | 2406.05274 | null | null | http://arxiv.org/pdf/2406.05274v1 | 2024-06-07T21:59:55Z | 2024-06-07T21:59:55Z | Behavior Structformer: Learning Players Representations with Structured
Tokenization | In this paper, we introduce the Behavior Structformer, a method for modeling user behavior using structured tokenization within a Transformer-based architecture. By converting tracking events into dense tokens, this approach enhances model training efficiency and effectiveness. We demonstrate its superior performance through ablation studies and benchmarking against traditional tabular and semi-structured baselines. The results indicate that structured tokenization with sequential processing significantly improves behavior modeling. | [
"['Oleg Smirnov' 'Labinot Polisi']"
] |
null | null | 2406.05276 | null | null | http://arxiv.org/pdf/2406.05276v2 | 2024-06-11T23:11:43Z | 2024-06-07T22:07:46Z | VTrans: Accelerating Transformer Compression with Variational
Information Bottleneck based Pruning | In recent years, there has been a growing emphasis on compressing large pre-trained transformer models for resource-constrained devices. However, traditional pruning methods often leave the embedding layer untouched, leading to model over-parameterization. Additionally, they require extensive compression time with large datasets to maintain performance in pruned models. To address these challenges, we propose VTrans, an iterative pruning framework guided by the Variational Information Bottleneck (VIB) principle. Our method compresses all structural components, including embeddings, attention heads, and layers using VIB-trained masks. This approach retains only essential weights in each layer, ensuring compliance with specified model size or computational constraints. Notably, our method achieves upto 70% more compression than prior state-of-the-art approaches, both task-agnostic and task-specific. We further propose faster variants of our method: Fast-VTrans utilizing only 3% of the data and Faster-VTrans, a time efficient alternative that involves exclusive finetuning of VIB masks, accelerating compression by upto 25 times with minimal performance loss compared to previous methods. Extensive experiments on BERT, ROBERTa, and GPT-2 models substantiate the efficacy of our method. Moreover, our method demonstrates scalability in compressing large models such as LLaMA-2-7B, achieving superior performance compared to previous pruning methods. Additionally, we use attention-based probing to qualitatively assess model redundancy and interpret the efficiency of our approach. Notably, our method considers heads with high attention to special and current tokens in un-pruned model as foremost candidates for pruning while retained heads are observed to attend more to task-critical keywords. | [
"['Oshin Dutta' 'Ritvik Gupta' 'Sumeet Agarwal']"
] |
null | null | 2406.05287 | null | null | http://arxiv.org/pdf/2406.05287v1 | 2024-06-07T23:00:02Z | 2024-06-07T23:00:02Z | Group-wise oracle-efficient algorithms for online multi-group learning | We study the problem of online multi-group learning, a learning model in which an online learner must simultaneously achieve small prediction regret on a large collection of (possibly overlapping) subsequences corresponding to a family of groups. Groups are subsets of the context space, and in fairness applications, they may correspond to subpopulations defined by expressive functions of demographic attributes. In contrast to previous work on this learning model, we consider scenarios in which the family of groups is too large to explicitly enumerate, and hence we seek algorithms that only access groups via an optimization oracle. In this paper, we design such oracle-efficient algorithms with sublinear regret under a variety of settings, including: (i) the i.i.d. setting, (ii) the adversarial setting with smoothed context distributions, and (iii) the adversarial transductive setting. | [
"['Samuel Deng' 'Daniel Hsu' 'Jingwen Liu']"
] |
null | null | 2406.05288 | null | null | http://arxiv.org/pdf/2406.05288v1 | 2024-06-07T23:04:53Z | 2024-06-07T23:04:53Z | Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at
Initialization | We introduce Optimal Eye Surgeon (OES), a framework for pruning and training deep image generator networks. Typically, untrained deep convolutional networks, which include image sampling operations, serve as effective image priors (Ulyanov et al., 2018). However, they tend to overfit to noise in image restoration tasks due to being overparameterized. OES addresses this by adaptively pruning networks at random initialization to a level of underparameterization. This process effectively captures low-frequency image components even without training, by just masking. When trained to fit noisy images, these pruned subnetworks, which we term Sparse-DIP, resist overfitting to noise. This benefit arises from underparameterization and the regularization effect of masking, constraining them in the manifold of image priors. We demonstrate that subnetworks pruned through OES surpass other leading pruning methods, such as the Lottery Ticket Hypothesis, which is known to be suboptimal for image recovery tasks (Wu et al., 2023). Our extensive experiments demonstrate the transferability of OES-masks and the characteristics of sparse-subnetworks for image generation. Code is available at https://github.com/Avra98/Optimal-Eye-Surgeon.git. | [
"['Avrajit Ghosh' 'Xitong Zhang' 'Kenneth K. Sun' 'Qing Qu'\n 'Saiprasad Ravishankar' 'Rongrong Wang']"
] |
null | null | 2406.05290 | null | null | http://arxiv.org/pdf/2406.05290v1 | 2024-06-07T23:25:13Z | 2024-06-07T23:25:13Z | Extremization to Fine Tune Physics Informed Neural Networks for Solving
Boundary Value Problems | We propose a novel method for fast and accurate training of physics-informed neural networks (PINNs) to find solutions to boundary value problems (BVPs) and initial boundary value problems (IBVPs). By combining the methods of training deep neural networks (DNNs) and Extreme Learning Machines (ELMs), we develop a model which has the expressivity of DNNs with the fine-tuning ability of ELMs. We showcase the superiority of our proposed method by solving several BVPs and IBVPs which include linear and non-linear ordinary differential equations (ODEs), partial differential equations (PDEs) and coupled PDEs. The examples we consider include a stiff coupled ODE system where traditional numerical methods fail, a 3+1D non-linear PDE, Kovasznay flow and Taylor-Green vortex solutions to incompressible Navier-Stokes equations and pure advection solution of 1+1 D compressible Euler equation. The Theory of Functional Connections (TFC) is used to exactly impose initial and boundary conditions (IBCs) of (I)BVPs on PINNs. We propose a modification to the TFC framework named Reduced TFC and show a significant improvement in the training and inference time of PINNs compared to IBCs imposed using TFC. Furthermore, Reduced TFC is shown to be able to generalize to more complex boundary geometries which is not possible with TFC. We also introduce a method of applying boundary conditions at infinity for BVPs and numerically solve the pure advection in 1+1 D Euler equations using these boundary conditions. | [
"['Abhiram Anand Thiruthummal' 'Sergiy Shelyag' 'Eun-jin Kim']"
] |
null | null | 2406.05295 | null | null | http://arxiv.org/pdf/2406.05295v1 | 2024-06-07T23:42:54Z | 2024-06-07T23:42:54Z | Information Geometry of Evolution of Neural Network Parameters While
Training | Artificial neural networks (ANNs) are powerful tools capable of approximating any arbitrary mathematical function, but their interpretability remains limited, rendering them as black box models. To address this issue, numerous methods have been proposed to enhance the explainability and interpretability of ANNs. In this study, we introduce the application of information geometric framework to investigate phase transition-like behavior during the training of ANNs and relate these transitions to overfitting in certain models. The evolution of ANNs during training is studied by looking at the probability distribution of its parameters. Information geometry utilizing the principles of differential geometry, offers a unique perspective on probability and statistics by considering probability density functions as points on a Riemannian manifold. We create this manifold using a metric based on Fisher information to define a distance and a velocity. By parameterizing this distance and velocity with training steps, we study how the ANN evolves as training progresses. Utilizing standard datasets like MNIST, FMNIST and CIFAR-10, we observe a transition in the motion on the manifold while training the ANN and this transition is identified with over-fitting in the ANN models considered. The information geometric transitions observed is shown to be mathematically similar to the phase transitions in physics. Preliminary results showing finite-size scaling behavior is also provided. This work contributes to the development of robust tools for improving the explainability and interpretability of ANNs, aiding in our understanding of the variability of the parameters these complex models exhibit during training. | [
"['Abhiram Anand Thiruthummal' 'Eun-jin Kim' 'Sergiy Shelyag']"
] |
null | null | 2406.05303 | null | null | http://arxiv.org/pdf/2406.05303v2 | 2024-06-22T00:33:22Z | 2024-06-08T00:07:16Z | Beyond Efficiency: Scaling AI Sustainably | Barroso's seminal contributions in energy-proportional warehouse-scale computing launched an era where modern datacenters have become more energy efficient and cost effective than ever before. At the same time, modern AI applications have driven ever-increasing demands in computing, highlighting the importance of optimizing efficiency across the entire deep learning model development cycle. This paper characterizes the carbon impact of AI, including both operational carbon emissions from training and inference as well as embodied carbon emissions from datacenter construction and hardware manufacturing. We highlight key efficiency optimization opportunities for cutting-edge AI technologies, from deep learning recommendation models to multi-modal generative AI tasks. To scale AI sustainably, we must also go beyond efficiency and optimize across the life cycle of computing infrastructures, from hardware manufacturing to datacenter operations and end-of-life processing for the hardware. | [
"['Carole-Jean Wu' 'Bilge Acun' 'Ramya Raghavendra' 'Kim Hazelwood']"
] |
null | null | 2406.05315 | null | null | http://arxiv.org/pdf/2406.05315v1 | 2024-06-08T01:27:19Z | 2024-06-08T01:27:19Z | Concept Formation and Alignment in Language Models: Bridging Statistical
Patterns in Latent Space to Concept Taxonomy | This paper explores the concept formation and alignment within the realm of language models (LMs). We propose a mechanism for identifying concepts and their hierarchical organization within the semantic representations learned by various LMs, encompassing a spectrum from early models like Glove to the transformer-based language models like ALBERT and T5. Our approach leverages the inherent structure present in the semantic embeddings generated by these models to extract a taxonomy of concepts and their hierarchical relationships. This investigation sheds light on how LMs develop conceptual understanding and opens doors to further research to improve their ability to reason and leverage real-world knowledge. We further conducted experiments and observed the possibility of isolating these extracted conceptual representations from the reasoning modules of the transformer-based LMs. The observed concept formation along with the isolation of conceptual representations from the reasoning modules can enable targeted token engineering to open the door for potential applications in knowledge transfer, explainable AI, and the development of more modular and conceptually grounded language models. | [
"['Mehrdad Khatir' 'Chandan K. Reddy']"
] |
null | null | 2406.05316 | null | null | http://arxiv.org/pdf/2406.05316v1 | 2024-06-08T01:32:44Z | 2024-06-08T01:32:44Z | C-Mamba: Channel Correlation Enhanced State Space Models for
Multivariate Time Series Forecasting | In recent years, significant progress has been made in multivariate time series forecasting using Linear-based, Transformer-based, and Convolution-based models. However, these approaches face notable limitations: linear forecasters struggle with representation capacities, attention mechanisms suffer from quadratic complexity, and convolutional models have a restricted receptive field. These constraints impede their effectiveness in modeling complex time series, particularly those with numerous variables. Additionally, many models adopt the Channel-Independent (CI) strategy, treating multivariate time series as uncorrelated univariate series while ignoring their correlations. For models considering inter-channel relationships, whether through the self-attention mechanism, linear combination, or convolution, they all incur high computational costs and focus solely on weighted summation relationships, neglecting potential proportional relationships between channels. In this work, we address these issues by leveraging the newly introduced state space model and propose textbf{C-Mamba}, a novel approach that captures cross-channel dependencies while maintaining linear complexity without losing the global receptive field. Our model consists of two key components: (i) channel mixup, where two channels are mixed to enhance the training sets; (ii) channel attention enhanced patch-wise Mamba encoder that leverages the ability of the state space models to capture cross-time dependencies and models correlations between channels by mining their weight relationships. Our model achieves state-of-the-art performance on seven real-world time series datasets. Moreover, the proposed mixup and attention strategy exhibits strong generalizability across other frameworks. | [
"['Chaolv Zeng' 'Zhanyu Liu' 'Guanjie Zheng' 'Linghe Kong']"
] |
null | null | 2406.05317 | null | null | http://arxiv.org/pdf/2406.05317v1 | 2024-06-08T01:35:11Z | 2024-06-08T01:35:11Z | LoCoCo: Dropping In Convolutions for Long Context Compression | This paper tackles the memory hurdle of processing long context sequences in Large Language Models (LLMs), by presenting a novel approach, Dropping In Convolutions for Long Context Compression (LoCoCo). LoCoCo employs only a fixed-size Key-Value (KV) cache, and can enhance efficiency in both inference and fine-tuning stages. Diverging from prior methods that selectively drop KV pairs based on heuristics, LoCoCo leverages a data-driven adaptive fusion technique, blending previous KV pairs with incoming tokens to minimize the loss of contextual information and ensure accurate attention modeling. This token integration is achieved through injecting one-dimensional convolutional kernels that dynamically calculate mixing weights for each KV cache slot. Designed for broad compatibility with existing LLM frameworks, LoCoCo allows for straightforward "drop-in" integration without needing architectural modifications, while incurring minimal tuning overhead. Experiments demonstrate that LoCoCo maintains consistently outstanding performance across various context lengths and can achieve a high context compression rate during both inference and fine-tuning phases. During inference, we successfully compressed up to 3482 tokens into a 128-size KV cache, while retaining comparable performance to the full sequence - an accuracy improvement of up to 0.2791 compared to baselines at the same cache size. During post-training tuning, we also effectively extended the context length from 4K to 32K using a KV cache of fixed size 512, achieving performance similar to fine-tuning with entire sequences. | [
"['Ruisi Cai' 'Yuandong Tian' 'Zhangyang Wang' 'Beidi Chen']"
] |
null | null | 2406.05320 | null | null | http://arxiv.org/pdf/2406.05320v1 | 2024-06-08T02:01:50Z | 2024-06-08T02:01:50Z | Deep Neural Networks are Adaptive to Function Regularity and Data
Distribution in Approximation and Estimation | Deep learning has exhibited remarkable results across diverse areas. To understand its success, substantial research has been directed towards its theoretical foundations. Nevertheless, the majority of these studies examine how well deep neural networks can model functions with uniform regularity. In this paper, we explore a different angle: how deep neural networks can adapt to different regularity in functions across different locations and scales and nonuniform data distributions. More precisely, we focus on a broad class of functions defined by nonlinear tree-based approximation. This class encompasses a range of function types, such as functions with uniform regularity and discontinuous functions. We develop nonparametric approximation and estimation theories for this function class using deep ReLU networks. Our results show that deep neural networks are adaptive to different regularity of functions and nonuniform data distributions at different locations and scales. We apply our results to several function classes, and derive the corresponding approximation and generalization errors. The validity of our results is demonstrated through numerical experiments. | [
"['Hao Liu' 'Jiahui Cheng' 'Wenjing Liao']"
] |
null | null | 2406.05328 | null | null | http://arxiv.org/pdf/2406.05328v1 | 2024-06-08T02:59:52Z | 2024-06-08T02:59:52Z | Hidden Question Representations Tell Non-Factuality Within and Across
Large Language Models | Despite the remarkable advance of large language models (LLMs), the prevalence of non-factual responses remains a common issue. This work studies non-factuality prediction (NFP), which predicts whether an LLM will generate non-factual responses to a question before the generation process. Previous efforts on NFP usually rely on extensive computation. In this work, we conduct extensive analysis to explore the capabilities of using a lightweight probe to elicit ``whether an LLM knows'' from the hidden representations of questions. Additionally, we discover that the non-factuality probe employs similar patterns for NFP across multiple LLMs. Motivated by the intriguing finding, we conduct effective transfer learning for cross-LLM NFP and propose a question-aligned strategy to ensure the efficacy of mini-batch based training. | [
"['Yanling Wang' 'Haoyang Li' 'Hao Zou' 'Jing Zhang' 'Xinlei He' 'Qi Li'\n 'Ke Xu']"
] |
null | null | 2406.05332 | null | null | http://arxiv.org/pdf/2406.05332v1 | 2024-06-08T03:17:48Z | 2024-06-08T03:17:48Z | Transformer Conformal Prediction for Time Series | We present a conformal prediction method for time series using the Transformer architecture to capture long-memory and long-range dependencies. Specifically, we use the Transformer decoder as a conditional quantile estimator to predict the quantiles of prediction residuals, which are used to estimate the prediction interval. We hypothesize that the Transformer decoder benefits the estimation of the prediction interval by learning temporal dependencies across past prediction residuals. Our comprehensive experiments using simulated and real data empirically demonstrate the superiority of the proposed method compared to the existing state-of-the-art conformal prediction methods. | [
"['Junghwan Lee' 'Chen Xu' 'Yao Xie']"
] |
null | null | 2406.05335 | null | null | http://arxiv.org/pdf/2406.05335v1 | 2024-06-08T03:37:05Z | 2024-06-08T03:37:05Z | Critical Phase Transition in a Large Language Model | The performance of large language models (LLMs) strongly depends on the textit{temperature} parameter. Empirically, at very low temperatures, LLMs generate sentences with clear repetitive structures, while at very high temperatures, generated sentences are often incomprehensible. In this study, using GPT-2, we numerically demonstrate that the difference between the two regimes is not just a smooth change but a phase transition with singular, divergent statistical quantities. Our extensive analysis shows that critical behaviors, such as a power-law decay of correlation in a text, emerge in the LLM at the transition temperature as well as in a natural language dataset. We also discuss that several statistical quantities characterizing the criticality should be useful to evaluate the performance of LLMs. | [
"['Kai Nakaishi' 'Yoshihiko Nishikawa' 'Koji Hukushima']"
] |
null | null | 2406.05346 | null | null | http://arxiv.org/pdf/2406.05346v2 | 2024-06-19T10:55:22Z | 2024-06-08T04:17:48Z | ProG: A Graph Prompt Learning Benchmark | Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional 'Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer issues, particularly in complex and few-shot settings. Graph prompt learning emerges as a promising alternative, leveraging lightweight prompts to manipulate data and fill the task gap by reformulating downstream tasks to the pretext. However, several critical challenges still remain: how to unify diverse graph prompt models, how to evaluate the quality of graph prompts, and to improve their usability for practical comparisons and selection. In response to these challenges, we introduce the first comprehensive benchmark for graph prompt learning. Our benchmark integrates SIX pre-training methods and FIVE state-of-the-art graph prompt techniques, evaluated across FIFTEEN diverse datasets to assess performance, flexibility, and efficiency. We also present 'ProG', an easy-to-use open-source library that streamlines the execution of various graph prompt models, facilitating objective evaluations. Additionally, we propose a unified framework that categorizes existing graph prompt methods into two main approaches: prompts as graphs and prompts as tokens. This framework enhances the applicability and comparison of graph prompt techniques. The code is available at: https://github.com/sheldonresearch/ProG. | [
"['Chenyi Zi' 'Haihong Zhao' 'Xiangguo Sun' 'Yiqing Lin' 'Hong Cheng'\n 'Jia Li']"
] |
null | null | 2406.05347 | null | null | http://arxiv.org/pdf/2406.05347v2 | 2024-06-11T02:42:17Z | 2024-06-08T04:23:57Z | MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative
Pre-Training | Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. The accuracy of protein structure predictions is often compromised for protein sequences that lack sufficient homologous information to construct high quality MSA. Although various methods have been proposed to generate virtual MSA under these conditions, they fall short in comprehensively capturing the intricate coevolutionary patterns within MSA or require guidance from external oracle models. Here we introduce MSAGPT, a novel approach to prompt protein structure predictions via MSA generative pretraining in the low MSA regime. MSAGPT employs a simple yet effective 2D evolutionary positional encoding scheme to model complex evolutionary patterns. Endowed by this, its flexible 1D MSA decoding framework facilitates zero or few shot learning. Moreover, we demonstrate that leveraging the feedback from AlphaFold2 can further enhance the model capacity via Rejective Fine tuning (RFT) and Reinforcement Learning from AF2 Feedback (RLAF). Extensive experiments confirm the efficacy of MSAGPT in generating faithful virtual MSA to enhance the structure prediction accuracy. The transfer learning capabilities also highlight its great potential for facilitating other protein tasks. | [
"['Bo Chen' 'Zhilei Bei' 'Xingyi Cheng' 'Pan Li' 'Jie Tang' 'Le Song']"
] |
null | null | 2406.05358 | null | null | http://arxiv.org/pdf/2406.05358v1 | 2024-06-08T05:27:01Z | 2024-06-08T05:27:01Z | Reinforcement Learning for Intensity Control: An Application to
Choice-Based Network Revenue Management | Intensity control is a type of continuous-time dynamic optimization problems with many important applications in Operations Research including queueing and revenue management. In this study, we adapt the reinforcement learning framework to intensity control using choice-based network revenue management as a case study, which is a classical problem in revenue management that features a large state space, a large action space and a continuous time horizon. We show that by utilizing the inherent discretization of the sample paths created by the jump points, a unique and defining feature of intensity control, one does not need to discretize the time horizon in advance, which was believed to be necessary because most reinforcement learning algorithms are designed for discrete-time problems. As a result, the computation can be facilitated and the discretization error is significantly reduced. We lay the theoretical foundation for the Monte Carlo and temporal difference learning algorithms for policy evaluation and develop policy gradient based actor critic algorithms for intensity control. Via a comprehensive numerical study, we demonstrate the benefit of our approach versus other state-of-the-art benchmarks. | [
"['Huiling Meng' 'Ningyuan Chen' 'Xuefeng Gao']"
] |
null | null | 2406.05362 | null | null | http://arxiv.org/pdf/2406.05362v1 | 2024-06-08T05:39:24Z | 2024-06-08T05:39:24Z | RAPID: Robust APT Detection and Investigation Using Context-Aware Deep
Learning | Advanced persistent threats (APTs) pose significant challenges for organizations, leading to data breaches, financial losses, and reputational damage. Existing provenance-based approaches for APT detection often struggle with high false positive rates, a lack of interpretability, and an inability to adapt to evolving system behavior. We introduce RAPID, a novel deep learning-based method for robust APT detection and investigation, leveraging context-aware anomaly detection and alert tracing. By utilizing self-supervised sequence learning and iteratively learned embeddings, our approach effectively adapts to dynamic system behavior. The use of provenance tracing both enriches the alerts and enhances the detection capabilities of our approach. Our extensive evaluation demonstrates RAPID's effectiveness and computational efficiency in real-world scenarios. In addition, RAPID achieves higher precision and recall than state-of-the-art methods, significantly reducing false positives. RAPID integrates contextual information and facilitates a smooth transition from detection to investigation, providing security teams with detailed insights to efficiently address APT threats. | [
"['Yonatan Amaru' 'Prasanna Wudali' 'Yuval Elovici' 'Asaf Shabtai']"
] |
null | null | 2406.05365 | null | null | http://arxiv.org/pdf/2406.05365v2 | 2024-06-24T07:39:26Z | 2024-06-08T06:04:55Z | CaLM: Contrasting Large and Small Language Models to Verify Grounded
Generation | Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses by accurately citing verifiable sources. However, existing methods, by either feeding LMs with raw or preprocessed materials, remain prone to errors. To address this, we introduce CaLM, a novel verification framework. CaLM leverages the insight that a robust grounded response should be consistent with information derived solely from its cited sources. Our framework empowers smaller LMs, which rely less on parametric memory and excel at processing relevant information given a query, to validate the output of larger LMs. Larger LM responses that closely align with the smaller LMs' output, which relies exclusively on cited documents, are verified. Responses showing discrepancies are iteratively refined through a feedback loop. Experiments on three open-domain question-answering datasets demonstrate significant performance gains of 1.5% to 7% absolute average without any required model fine-tuning. | [
"['I-Hung Hsu' 'Zifeng Wang' 'Long T. Le' 'Lesly Miculicich' 'Nanyun Peng'\n 'Chen-Yu Lee' 'Tomas Pfister']"
] |
null | null | 2406.05366 | null | null | http://arxiv.org/pdf/2406.05366v1 | 2024-06-08T06:06:20Z | 2024-06-08T06:06:20Z | Regret Bounds for Episodic Risk-Sensitive Linear Quadratic Regulator | Risk-sensitive linear quadratic regulator is one of the most fundamental problems in risk-sensitive optimal control. In this paper, we study online adaptive control of risk-sensitive linear quadratic regulator in the finite horizon episodic setting. We propose a simple least-squares greedy algorithm and show that it achieves $widetilde{mathcal{O}}(log N)$ regret under a specific identifiability assumption, where $N$ is the total number of episodes. If the identifiability assumption is not satisfied, we propose incorporating exploration noise into the least-squares-based algorithm, resulting in an algorithm with $widetilde{mathcal{O}}(sqrt{N})$ regret. To our best knowledge, this is the first set of regret bounds for episodic risk-sensitive linear quadratic regulator. Our proof relies on perturbation analysis of less-standard Riccati equations for risk-sensitive linear quadratic control, and a delicate analysis of the loss in the risk-sensitive performance criterion due to applying the suboptimal controller in the online learning process. | [
"['Wenhao Xu' 'Xuefeng Gao' 'Xuedong He']"
] |
null | null | 2406.05369 | null | null | http://arxiv.org/pdf/2406.05369v1 | 2024-06-08T06:27:26Z | 2024-06-08T06:27:26Z | Venn Diagram Prompting : Accelerating Comprehension with Scaffolding
Effect | We introduce Venn Diagram (VD) Prompting, an innovative prompting technique which allows Large Language Models (LLMs) to combine and synthesize information across complex, diverse and long-context documents in knowledge-intensive question-answering tasks. Generating answers from multiple documents involves numerous steps to extract relevant and unique information and amalgamate it into a cohesive response. To improve the quality of the final answer, multiple LLM calls or pretrained models are used to perform different tasks such as summarization, reorganization and customization. The approach covered in the paper focuses on replacing the multi-step strategy via a single LLM call using VD prompting. Our proposed technique also aims to eliminate the inherent position bias in the LLMs, enhancing consistency in answers by removing sensitivity to the sequence of input information. It overcomes the challenge of inconsistency traditionally associated with varying input sequences. We also explore the practical applications of the VD prompt based on our examination of the prompt's outcomes. In the experiments performed on four public benchmark question-answering datasets, VD prompting continually matches or surpasses the performance of a meticulously crafted instruction prompt which adheres to optimal guidelines and practices. | [
"['Sakshi Mahendru' 'Tejul Pandit']"
] |
null | null | 2406.05372 | null | null | http://arxiv.org/pdf/2406.05372v1 | 2024-06-08T06:45:19Z | 2024-06-08T06:45:19Z | Bridging the Gap: Rademacher Complexity in Robust and Standard
Generalization | Training Deep Neural Networks (DNNs) with adversarial examples often results in poor generalization to test-time adversarial data. This paper investigates this issue, known as adversarially robust generalization, through the lens of Rademacher complexity. Building upon the studies by Khim and Loh (2018); Yin et al. (2019), numerous works have been dedicated to this problem, yet achieving a satisfactory bound remains an elusive goal. Existing works on DNNs either apply to a surrogate loss instead of the robust loss or yield bounds that are notably looser compared to their standard counterparts. In the latter case, the bounds have a higher dependency on the width $m$ of the DNNs or the dimension $d$ of the data, with an extra factor of at least $mathcal{O}(sqrt{m})$ or $mathcal{O}(sqrt{d})$. This paper presents upper bounds for adversarial Rademacher complexity of DNNs that match the best-known upper bounds in standard settings, as established in the work of Bartlett et al. (2017), with the dependency on width and dimension being $mathcal{O}(ln(dm))$. The central challenge addressed is calculating the covering number of adversarial function classes. We aim to construct a new cover that possesses two properties: 1) compatibility with adversarial examples, and 2) precision comparable to covers used in standard settings. To this end, we introduce a new variant of covering number called the emph{uniform covering number}, specifically designed and proven to reconcile these two properties. Consequently, our method effectively bridges the gap between Rademacher complexity in robust and standard generalization. | [
"['Jiancong Xiao' 'Ruoyu Sun' 'Qi Long' 'Weijie J. Su']"
] |
null | null | 2406.05375 | null | null | http://arxiv.org/pdf/2406.05375v1 | 2024-06-08T07:00:31Z | 2024-06-08T07:00:31Z | LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause
Analysis | Root cause analysis (RCA) is crucial for enhancing the reliability and performance of complex systems. However, progress in this field has been hindered by the lack of large-scale, open-source datasets tailored for RCA. To bridge this gap, we introduce LEMMA-RCA, a large dataset designed for diverse RCA tasks across multiple domains and modalities. LEMMA-RCA features various real-world fault scenarios from IT and OT operation systems, encompassing microservices, water distribution, and water treatment systems, with hundreds of system entities involved. We evaluate the quality of LEMMA-RCA by testing the performance of eight baseline methods on this dataset under various settings, including offline and online modes as well as single and multiple modalities. Our experimental results demonstrate the high quality of LEMMA-RCA. The dataset is publicly available at https://lemma-rca.github.io/. | [
"['Lecheng Zheng' 'Zhengzhang Chen' 'Dongjie Wang' 'Chengyuan Deng'\n 'Reon Matsuoka' 'Haifeng Chen']"
] |
null | null | 2406.05376 | null | null | http://arxiv.org/pdf/2406.05376v2 | 2024-06-11T08:20:26Z | 2024-06-08T07:05:26Z | Adversarial flows: A gradient flow characterization of adversarial
attacks | A popular method to perform adversarial attacks on neuronal networks is the so-called fast gradient sign method and its iterative variant. In this paper, we interpret this method as an explicit Euler discretization of a differential inclusion, where we also show convergence of the discretization to the associated gradient flow. To do so, we consider the concept of p-curves of maximal slope in the case $p=infty$. We prove existence of $infty$-curves of maximum slope and derive an alternative characterization via differential inclusions. Furthermore, we also consider Wasserstein gradient flows for potential energies, where we show that curves in the Wasserstein space can be characterized by a representing measure on the space of curves in the underlying Banach space, which fulfill the differential inclusion. The application of our theory to the finite-dimensional setting is twofold: On the one hand, we show that a whole class of normalized gradient descent methods (in particular signed gradient descent) converge, up to subsequences, to the flow, when sending the step size to zero. On the other hand, in the distributional setting, we show that the inner optimization task of adversarial training objective can be characterized via $infty$-curves of maximum slope on an appropriate optimal transport space. | [
"['Lukas Weigand' 'Tim Roith' 'Martin Burger']"
] |
null | null | 2406.05391 | null | null | http://arxiv.org/pdf/2406.05391v1 | 2024-06-08T07:48:16Z | 2024-06-08T07:48:16Z | DUPLEX: Dual GAT for Complex Embedding of Directed Graphs | Current directed graph embedding methods build upon undirected techniques but often inadequately capture directed edge information, leading to challenges such as: (1) Suboptimal representations for nodes with low in/out-degrees, due to the insufficient neighbor interactions; (2) Limited inductive ability for representing new nodes post-training; (3) Narrow generalizability, as training is overly coupled with specific tasks. In response, we propose DUPLEX, an inductive framework for complex embeddings of directed graphs. It (1) leverages Hermitian adjacency matrix decomposition for comprehensive neighbor integration, (2) employs a dual GAT encoder for directional neighbor modeling, and (3) features two parameter-free decoders to decouple training from particular tasks. DUPLEX outperforms state-of-the-art models, especially for nodes with sparse connectivity, and demonstrates robust inductive capability and adaptability across various tasks. The code is available at https://github.com/alipay/DUPLEX. | [
"['Zhaoru Ke' 'Hang Yu' 'Jianguo Li' 'Haipeng Zhang']"
] |
null | null | 2406.05392 | null | null | http://arxiv.org/pdf/2406.05392v1 | 2024-06-08T07:55:01Z | 2024-06-08T07:55:01Z | Deconstructing The Ethics of Large Language Models from Long-standing
Issues to New-emerging Dilemmas | Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. However, this progress has also intensified ethical concerns, impacting the deployment of LLMs in everyday contexts. This paper provides a comprehensive survey of ethical challenges associated with LLMs, from longstanding issues such as copyright infringement, systematic bias, and data privacy, to emerging problems like truthfulness and social norms. We critically analyze existing research aimed at understanding, examining, and mitigating these ethical risks. Our survey underscores integrating ethical standards and societal values into the development of LLMs, thereby guiding the development of responsible and ethically aligned language models. | [
"['Chengyuan Deng' 'Yiqun Duan' 'Xin Jin' 'Heng Chang' 'Yijun Tian'\n 'Han Liu' 'Henry Peng Zou' 'Yiqiao Jin' 'Yijia Xiao' 'Yichen Wang'\n 'Shenghao Wu' 'Zongxing Xie' 'Kuofeng Gao' 'Sihong He' 'Jun Zhuang'\n 'Lu Cheng' 'Haohan Wang']"
] |
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