categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2402.18610
| null | null |
http://arxiv.org/pdf/2402.18610v1
|
2024-02-28T15:10:25Z
|
2024-02-28T15:10:25Z
|
Why Attention Graphs Are All We Need: Pioneering Hierarchical
Classification of Hematologic Cell Populations with LeukoGraph
|
In the complex landscape of hematologic samples such as peripheral blood or bone marrow, cell classification, delineating diverse populations into a hierarchical structure, presents profound challenges. This study presents LeukoGraph, a recently developed framework designed explicitly for this purpose employing graph attention networks (GATs) to navigate hierarchical classification (HC) complexities. Notably, LeukoGraph stands as a pioneering effort, marking the application of graph neural networks (GNNs) for hierarchical inference on graphs, accommodating up to one million nodes and millions of edges, all derived from flow cytometry data. LeukoGraph intricately addresses a classification paradigm where for example four different cell populations undergo flat categorization, while a fifth diverges into two distinct child branches, exemplifying the nuanced hierarchical structure inherent in complex datasets. The technique is more general than this example. A hallmark achievement of LeukoGraph is its F-score of 98%, significantly outclassing prevailing state-of-the-art methodologies. Crucially, LeukoGraph's prowess extends beyond theoretical innovation, showcasing remarkable precision in predicting both flat and hierarchical cell types across flow cytometry datasets from 30 distinct patients. This precision is further underscored by LeukoGraph's ability to maintain a correct label ratio, despite the inherent challenges posed by hierarchical classifications.
|
[
"['Fatemeh Nassajian Mojarrad' 'Lorenzo Bini' 'Thomas Matthes'\n 'Stéphane Marchand-Maillet']"
] |
null | null |
2402.18611
| null | null |
http://arxiv.org/pdf/2402.18611v1
|
2024-02-28T15:15:38Z
|
2024-02-28T15:15:38Z
|
HemaGraph: Breaking Barriers in Hematologic Single Cell Classification
with Graph Attention
|
In the realm of hematologic cell populations classification, the intricate patterns within flow cytometry data necessitate advanced analytical tools. This paper presents 'HemaGraph', a novel framework based on Graph Attention Networks (GATs) for single-cell multi-class classification of hematological cells from flow cytometry data. Harnessing the power of GATs, our method captures subtle cell relationships, offering highly accurate patient profiling. Based on evaluation of data from 30 patients, HemaGraph demonstrates classification performance across five different cell classes, outperforming traditional methodologies and state-of-the-art methods. Moreover, the uniqueness of this framework lies in the training and testing phase of HemaGraph, where it has been applied for extremely large graphs, containing up to hundreds of thousands of nodes and two million edges, to detect low frequency cell populations (e.g. 0.01% for one population), with accuracies reaching 98%. Our findings underscore the potential of HemaGraph in improving hematoligic multi-class classification, paving the way for patient-personalized interventions. To the best of our knowledge, this is the first effort to use GATs, and Graph Neural Networks (GNNs) in general, to classify cell populations from single-cell flow cytometry data. We envision applying this method to single-cell data from larger cohort of patients and on other hematologic diseases.
|
[
"['Lorenzo Bini' 'Fatemeh Nassajian Mojarrad' 'Thomas Matthes'\n 'Stéphane Marchand-Maillet']"
] |
null | null |
2402.18612
| null | null |
http://arxiv.org/pdf/2402.18612v1
|
2024-02-28T15:29:02Z
|
2024-02-28T15:29:02Z
|
Understanding random forests and overfitting: a visualization and
simulation study
|
Random forests have become popular for clinical risk prediction modelling. In a case study on predicting ovarian malignancy, we observed training c-statistics close to 1. Although this suggests overfitting, performance was competitive on test data. We aimed to understand the behaviour of random forests by (1) visualizing data space in three real world case studies and (2) a simulation study. For the case studies, risk estimates were visualised using heatmaps in a 2-dimensional subspace. The simulation study included 48 logistic data generating mechanisms (DGM), varying the predictor distribution, the number of predictors, the correlation between predictors, the true c-statistic and the strength of true predictors. For each DGM, 1000 training datasets of size 200 or 4000 were simulated and RF models trained with minimum node size 2 or 20 using ranger package, resulting in 192 scenarios in total. The visualizations suggested that the model learned spikes of probability around events in the training set. A cluster of events created a bigger peak, isolated events local peaks. In the simulation study, median training c-statistics were between 0.97 and 1 unless there were 4 or 16 binary predictors with minimum node size 20. Median test c-statistics were higher with higher events per variable, higher minimum node size, and binary predictors. Median training slopes were always above 1, and were not correlated with median test slopes across scenarios (correlation -0.11). Median test slopes were higher with higher true c-statistic, higher minimum node size, and higher sample size. Random forests learn local probability peaks that often yield near perfect training c-statistics without strongly affecting c-statistics on test data. When the aim is probability estimation, the simulation results go against the common recommendation to use fully grown trees in random forest models.
|
[
"['Lasai Barreñada' 'Paula Dhiman' 'Dirk Timmerman' 'Anne-Laure Boulesteix'\n 'Ben Van Calster']"
] |
null | null |
2402.18614
| null | null |
http://arxiv.org/pdf/2402.18614v1
|
2024-02-28T15:52:30Z
|
2024-02-28T15:52:30Z
|
Deep Neural Network Models Trained With A Fixed Random Classifier
Transfer Better Across Domains
|
The recently discovered Neural collapse (NC) phenomenon states that the last-layer weights of Deep Neural Networks (DNN), converge to the so-called Equiangular Tight Frame (ETF) simplex, at the terminal phase of their training. This ETF geometry is equivalent to vanishing within-class variability of the last layer activations. Inspired by NC properties, we explore in this paper the transferability of DNN models trained with their last layer weight fixed according to ETF. This enforces class separation by eliminating class covariance information, effectively providing implicit regularization. We show that DNN models trained with such a fixed classifier significantly improve transfer performance, particularly on out-of-domain datasets. On a broad range of fine-grained image classification datasets, our approach outperforms i) baseline methods that do not perform any covariance regularization (up to 22%), as well as ii) methods that explicitly whiten covariance of activations throughout training (up to 19%). Our findings suggest that DNNs trained with fixed ETF classifiers offer a powerful mechanism for improving transfer learning across domains.
|
[
"['Hafiz Tiomoko Ali' 'Umberto Michieli' 'Ji Joong Moon' 'Daehyun Kim'\n 'Mete Ozay']"
] |
null | null |
2402.18617
| null | null |
http://arxiv.org/pdf/2402.18617v1
|
2024-02-28T17:44:02Z
|
2024-02-28T17:44:02Z
|
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
|
Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environment directly. Recent research has explored various ways to enhance offline learning efficiency by considering the characteristics (e.g., expertise level or multiple demonstrators) of the dataset. However, a different approach is necessary in the context of zero-sum games, where outcomes vary significantly based on the strategy of the opponent. In this study, we introduce a novel approach that uses unsupervised learning techniques to estimate the exploited level of each trajectory from the offline dataset of zero-sum games made by diverse demonstrators. Subsequently, we incorporate the estimated exploited level into the offline learning to maximize the influence of the dominant strategy. Our method enables interpretable exploited level estimation in multiple zero-sum games and effectively identifies dominant strategy data. Also, our exploited level augmented offline learning significantly enhances the original offline learning algorithms including imitation learning and offline reinforcement learning for zero-sum games.
|
[
"['Shiqi Lei' 'Kanghoon Lee' 'Linjing Li' 'Jinkyoo Park' 'Jiachen Li']"
] |
null | null |
2402.18630
| null | null |
http://arxiv.org/abs/2402.18630v1
|
2024-02-28T19:00:01Z
|
2024-02-28T19:00:01Z
|
GNSS Positioning using Cost Function Regulated Multilateration and Graph
Neural Networks
|
In urban environments, where line-of-sight signals from GNSS satellites are frequently blocked by high-rise objects, GNSS receivers are subject to large errors in measuring satellite ranges. Heuristic methods are commonly used to estimate these errors and reduce the impact of noisy measurements on localization accuracy. In our work, we replace these error estimation heuristics with a deep learning model based on Graph Neural Networks. Additionally, by analyzing the cost function of the multilateration process, we derive an optimal method to utilize the estimated errors. Our approach guarantees that the multilateration converges to the receiver's location as the error estimation accuracy increases. We evaluate our solution on a real-world dataset containing more than 100k GNSS epochs, collected from multiple cities with diverse characteristics. The empirical results show improvements from 40% to 80% in the horizontal localization error against recent deep learning baselines as well as classical localization approaches.
|
[
"['Amir Jalalirad' 'Davide Belli' 'Bence Major' 'Songwon Jee'\n 'Himanshu Shah' 'Will Morrison']"
] |
null | null |
2402.18651
| null | null |
http://arxiv.org/pdf/2402.18651v1
|
2024-02-28T19:00:36Z
|
2024-02-28T19:00:36Z
|
Quantifying Human Priors over Social and Navigation Networks
|
Human knowledge is largely implicit and relational -- do we have a friend in common? can I walk from here to there? In this work, we leverage the combinatorial structure of graphs to quantify human priors over such relational data. Our experiments focus on two domains that have been continuously relevant over evolutionary timescales: social interaction and spatial navigation. We find that some features of the inferred priors are remarkably consistent, such as the tendency for sparsity as a function of graph size. Other features are domain-specific, such as the propensity for triadic closure in social interactions. More broadly, our work demonstrates how nonclassical statistical analysis of indirect behavioral experiments can be used to efficiently model latent biases in the data.
|
[
"['Gecia Bravo-Hermsdorff']"
] |
null | null |
2402.18668
| null | null |
http://arxiv.org/pdf/2402.18668v1
|
2024-02-28T19:28:27Z
|
2024-02-28T19:28:27Z
|
Simple linear attention language models balance the recall-throughput
tradeoff
|
Recent work has shown that attention-based language models excel at recall, the ability to ground generations in tokens previously seen in context. However, the efficiency of attention-based models is bottle-necked during inference by the KV-cache's aggressive memory consumption. In this work, we explore whether we can improve language model efficiency (e.g. by reducing memory consumption) without compromising on recall. By applying experiments and theory to a broad set of architectures, we identify a key tradeoff between a model's state size and recall ability. We show that efficient alternatives to attention (e.g. H3, Mamba, RWKV) maintain a fixed-size recurrent state, but struggle at recall. We propose BASED a simple architecture combining linear and sliding window attention. By varying BASED window size and linear attention feature dimension, we can dial the state size and traverse the pareto frontier of the recall-memory tradeoff curve, recovering the full quality of attention on one end and the small state size of attention-alternatives on the other. We train language models up to 1.3b parameters and show that BASED matches the strongest sub-quadratic models (e.g. Mamba) in perplexity and outperforms them on real-world recall-intensive tasks by 6.22 accuracy points. Implementations of linear attention are often less efficient than optimized standard attention implementations. To make BASED competitive, we develop IO-aware algorithms that enable 24x higher throughput on language generation than FlashAttention-2, when generating 1024 tokens using 1.3b parameter models. Code for this work is provided at: https://github.com/HazyResearch/based.
|
[
"['Simran Arora' 'Sabri Eyuboglu' 'Michael Zhang' 'Aman Timalsina'\n 'Silas Alberti' 'Dylan Zinsley' 'James Zou' 'Atri Rudra' 'Christopher Ré']"
] |
null | null |
2402.18679
| null | null |
http://arxiv.org/pdf/2402.18679v3
|
2024-03-12T17:26:53Z
|
2024-02-28T19:49:55Z
|
Data Interpreter: An LLM Agent For Data Science
|
Large Language Model (LLM)-based agents have demonstrated remarkable effectiveness. However, their performance can be compromised in data science scenarios that require real-time data adjustment, expertise in optimization due to complex dependencies among various tasks, and the ability to identify logical errors for precise reasoning. In this study, we introduce the Data Interpreter, a solution designed to solve with code that emphasizes three pivotal techniques to augment problem-solving in data science: 1) dynamic planning with hierarchical graph structures for real-time data adaptability;2) tool integration dynamically to enhance code proficiency during execution, enriching the requisite expertise;3) logical inconsistency identification in feedback, and efficiency enhancement through experience recording. We evaluate the Data Interpreter on various data science and real-world tasks. Compared to open-source baselines, it demonstrated superior performance, exhibiting significant improvements in machine learning tasks, increasing from 0.86 to 0.95. Additionally, it showed a 26% increase in the MATH dataset and a remarkable 112% improvement in open-ended tasks. The solution will be released at https://github.com/geekan/MetaGPT.
|
[
"['Sirui Hong' 'Yizhang Lin' 'Bang Liu' 'Bangbang Liu' 'Binhao Wu'\n 'Danyang Li' 'Jiaqi Chen' 'Jiayi Zhang' 'Jinlin Wang' 'Li Zhang'\n 'Lingyao Zhang' 'Min Yang' 'Mingchen Zhuge' 'Taicheng Guo' 'Tuo Zhou'\n 'Wei Tao' 'Wenyi Wang' 'Xiangru Tang' 'Xiangtao Lu' 'Xiawu Zheng'\n 'Xinbing Liang' 'Yaying Fei' 'Yuheng Cheng' 'Zongze Xu' 'Chenglin Wu']"
] |
null | null |
2402.18689
| null | null |
http://arxiv.org/pdf/2402.18689v1
|
2024-02-28T20:09:14Z
|
2024-02-28T20:09:14Z
|
The VOROS: Lifting ROC curves to 3D
|
The area under the ROC curve is a common measure that is often used to rank the relative performance of different binary classifiers. However, as has been also previously noted, it can be a measure that ill-captures the benefits of different classifiers when either the true class values or misclassification costs are highly unbalanced between the two classes. We introduce a third dimension to capture these costs, and lift the ROC curve to a ROC surface in a natural way. We study both this surface and introduce the VOROS, the volume over this ROC surface, as a 3D generalization of the 2D area under the ROC curve. For problems where there are only bounds on the expected costs or class imbalances, we restrict consideration to the volume of the appropriate subregion of the ROC surface. We show how the VOROS can better capture the costs of different classifiers on both a classical and a modern example dataset.
|
[
"['Christopher Ratigan' 'Lenore Cowen']"
] |
null | null |
2402.18697
| null | null |
http://arxiv.org/pdf/2402.18697v1
|
2024-02-28T20:24:56Z
|
2024-02-28T20:24:56Z
|
Inferring Dynamic Networks from Marginals with Iterative Proportional
Fitting
|
A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i.e., row and column sums). Prior approaches to this problem have repurposed the classic iterative proportional fitting (IPF) procedure, also known as Sinkhorn's algorithm, with promising empirical results. However, the statistical foundation for using IPF has not been well understood: under what settings does IPF provide principled estimation of a dynamic network from its marginals, and how well does it estimate the network? In this work, we establish such a setting, by identifying a generative network model whose maximum likelihood estimates are recovered by IPF. Our model both reveals implicit assumptions on the use of IPF in such settings and enables new analyses, such as structure-dependent error bounds on IPF's parameter estimates. When IPF fails to converge on sparse network data, we introduce a principled algorithm that guarantees IPF converges under minimal changes to the network structure. Finally, we conduct experiments with synthetic and real-world data, which demonstrate the practical value of our theoretical and algorithmic contributions.
|
[
"['Serina Chang' 'Frederic Koehler' 'Zhaonan Qu' 'Jure Leskovec'\n 'Johan Ugander']"
] |
null | null |
2402.18700
| null | null |
http://arxiv.org/pdf/2402.18700v2
|
2024-04-02T02:38:31Z
|
2024-02-28T20:41:21Z
|
Learning to Compress Prompt in Natural Language Formats
|
Large language models (LLMs) are great at processing multiple natural language processing tasks, but their abilities are constrained by inferior performance with long context, slow inference speed, and the high cost of computing the results. Deploying LLMs with precise and informative context helps users process large-scale datasets more effectively and cost-efficiently. Existing works rely on compressing long prompt contexts into soft prompts. However, soft prompt compression encounters limitations in transferability across different LLMs, especially API-based LLMs. To this end, this work aims to compress lengthy prompts in the form of natural language with LLM transferability. This poses two challenges: (i) Natural Language (NL) prompts are incompatible with back-propagation, and (ii) NL prompts lack flexibility in imposing length constraints. In this work, we propose a Natural Language Prompt Encapsulation (Nano-Capsulator) framework compressing original prompts into NL formatted Capsule Prompt while maintaining the prompt utility and transferability. Specifically, to tackle the first challenge, the Nano-Capsulator is optimized by a reward function that interacts with the proposed semantics preserving loss. To address the second question, the Nano-Capsulator is optimized by a reward function featuring length constraints. Experimental results demonstrate that the Capsule Prompt can reduce 81.4% of the original length, decrease inference latency up to 4.5x, and save 80.1% of budget overheads while providing transferability across diverse LLMs and different datasets.
|
[
"['Yu-Neng Chuang' 'Tianwei Xing' 'Chia-Yuan Chang' 'Zirui Liu' 'Xun Chen'\n 'Xia Hu']"
] |
null | null |
2402.18724
| null | null |
http://arxiv.org/pdf/2402.18724v1
|
2024-02-28T21:47:30Z
|
2024-02-28T21:47:30Z
|
Learning Associative Memories with Gradient Descent
|
This work focuses on the training dynamics of one associative memory module storing outer products of token embeddings. We reduce this problem to the study of a system of particles, which interact according to properties of the data distribution and correlations between embeddings. Through theory and experiments, we provide several insights. In overparameterized regimes, we obtain logarithmic growth of the ``classification margins.'' Yet, we show that imbalance in token frequencies and memory interferences due to correlated embeddings lead to oscillatory transitory regimes. The oscillations are more pronounced with large step sizes, which can create benign loss spikes, although these learning rates speed up the dynamics and accelerate the asymptotic convergence. In underparameterized regimes, we illustrate how the cross-entropy loss can lead to suboptimal memorization schemes. Finally, we assess the validity of our findings on small Transformer models.
|
[
"['Vivien Cabannes' 'Berfin Simsek' 'Alberto Bietti']"
] |
null | null |
2402.18726
| null | null |
http://arxiv.org/pdf/2402.18726v1
|
2024-02-28T22:02:10Z
|
2024-02-28T22:02:10Z
|
Unveiling Privacy, Memorization, and Input Curvature Links
|
Deep Neural Nets (DNNs) have become a pervasive tool for solving many emerging problems. However, they tend to overfit to and memorize the training set. Memorization is of keen interest since it is closely related to several concepts such as generalization, noisy learning, and privacy. To study memorization, Feldman (2019) proposed a formal score, however its computational requirements limit its practical use. Recent research has shown empirical evidence linking input loss curvature (measured by the trace of the loss Hessian w.r.t inputs) and memorization. It was shown to be ~3 orders of magnitude more efficient than calculating the memorization score. However, there is a lack of theoretical understanding linking memorization with input loss curvature. In this paper, we not only investigate this connection but also extend our analysis to establish theoretical links between differential privacy, memorization, and input loss curvature. First, we derive an upper bound on memorization characterized by both differential privacy and input loss curvature. Second, we present a novel insight showing that input loss curvature is upper-bounded by the differential privacy parameter. Our theoretical findings are further empirically validated using deep models on CIFAR and ImageNet datasets, showing a strong correlation between our theoretical predictions and results observed in practice.
|
[
"['Deepak Ravikumar' 'Efstathia Soufleri' 'Abolfazl Hashemi' 'Kaushik Roy']"
] |
null | null |
2402.18729
| null | null |
http://arxiv.org/pdf/2402.18729v1
|
2024-02-28T22:19:55Z
|
2024-02-28T22:19:55Z
|
A Priori Uncertainty Quantification of Reacting Turbulence Closure
Models using Bayesian Neural Networks
|
While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical simulation (DNS) create opportunities to leverage data-driven modeling techniques. Albeit flexible, data-driven models still depend on the dataset and the functional form of the model chosen. Increased adoption of such models requires reliable uncertainty estimates both in the data-informed and out-of-distribution regimes. In this work, we employ Bayesian neural networks (BNNs) to capture both epistemic and aleatoric uncertainties in a reacting flow model. In particular, we model the filtered progress variable scalar dissipation rate which plays a key role in the dynamics of turbulent premixed flames. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. We also propose a method for the incorporation of out-of-distribution information in a BNN. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.
|
[
"['Graham Pash' 'Malik Hassanaly' 'Shashank Yellapantula']"
] |
null | null |
2402.18732
| null | null |
http://arxiv.org/pdf/2402.18732v1
|
2024-02-28T22:25:02Z
|
2024-02-28T22:25:02Z
|
GAIA: Categorical Foundations of Generative AI
|
In this paper, we propose GAIA, a generative AI architecture based on category theory. GAIA is based on a hierarchical model where modules are organized as a simplicial complex. Each simplicial complex updates its internal parameters biased on information it receives from its superior simplices and in turn relays updates to its subordinate sub-simplices. Parameter updates are formulated in terms of lifting diagrams over simplicial sets, where inner and outer horn extensions correspond to different types of learning problems. Backpropagation is modeled as an endofunctor over the category of parameters, leading to a coalgebraic formulation of deep learning.
|
[
"['Sridhar Mahadevan']"
] |
null | null |
2402.18734
| null | null |
http://arxiv.org/pdf/2402.18734v1
|
2024-02-28T22:27:49Z
|
2024-02-28T22:27:49Z
|
Priority Sampling of Large Language Models for Compilers
|
Large language models show great potential in generating and optimizing code. Widely used sampling methods such as Nucleus Sampling increase the diversity of generation but often produce repeated samples for low temperatures and incoherent samples for high temperatures. Furthermore, the temperature coefficient has to be tuned for each task, limiting its usability. We present Priority Sampling, a simple and deterministic sampling technique that produces unique samples ordered by the model's confidence. Each new sample expands the unexpanded token with the highest probability in the augmented search tree. Additionally, Priority Sampling supports generation based on regular expression that provides a controllable and structured exploration process. Priority Sampling outperforms Nucleus Sampling for any number of samples, boosting the performance of the original model from 2.87% to 5% improvement over -Oz. Moreover, it outperforms the autotuner used for the generation of labels for the training of the original model in just 30 samples.
|
[
"['Dejan Grubisic' 'Chris Cummins' 'Volker Seeker' 'Hugh Leather']"
] |
null | null |
2402.18746
| null | null |
http://arxiv.org/pdf/2402.18746v1
|
2024-02-28T23:00:57Z
|
2024-02-28T23:00:57Z
|
Accelerating Computer Architecture Simulation through Machine Learning
|
This paper presents our approach to accelerate computer architecture simulation by leveraging machine learning techniques. Traditional computer architecture simulations are time-consuming, making it challenging to explore different design choices efficiently. Our proposed model utilizes a combination of application features and micro-architectural features to predict the performance of an application. These features are derived from simulations of a small portion of the application. We demonstrate the effectiveness of our approach by building and evaluating a machine learning model that offers significant speedup in architectural exploration. This model demonstrates the ability to predict IPC values for the testing data with a root mean square error of less than 0.1.
|
[
"['Wajid Ali' 'Ayaz Akram']"
] |
null | null |
2402.18751
| null | null |
http://arxiv.org/pdf/2402.18751v1
|
2024-02-28T23:18:15Z
|
2024-02-28T23:18:15Z
|
Multi-Sensor and Multi-temporal High-Throughput Phenotyping for
Monitoring and Early Detection of Water-Limiting Stress in Soybean
|
Soybean production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events. Water limiting stress, i.e. drought, emerges as a significant risk for soybean production, underscoring the need for advancements in stress monitoring for crop breeding and production. This project combines multi-modal information to identify the most effective and efficient automated methods to investigate drought response. We investigated a set of diverse soybean accessions using multiple sensors in a time series high-throughput phenotyping manner to: (1) develop a pipeline for rapid classification of soybean drought stress symptoms, and (2) investigate methods for early detection of drought stress. We utilized high-throughput time-series phenotyping using UAVs and sensors in conjunction with machine learning (ML) analytics, which offered a swift and efficient means of phenotyping. The red-edge and green bands were most effective to classify canopy wilting stress. The Red-Edge Chlorophyll Vegetation Index (RECI) successfully differentiated susceptible and tolerant soybean accessions prior to visual symptom development. We report pre-visual detection of soybean wilting using a combination of different vegetation indices. These results can contribute to early stress detection methodologies and rapid classification of drought responses in screening nurseries for breeding and production applications.
|
[
"['Sarah E. Jones' 'Timilehin Ayanlade' 'Benjamin Fallen'\n 'Talukder Z. Jubery' 'Arti Singh' 'Baskar Ganapathysubramanian'\n 'Soumik Sarkar' 'Asheesh K. Singh']"
] |
null | null |
2402.18752
| null | null |
http://arxiv.org/pdf/2402.18752v1
|
2024-02-28T23:26:27Z
|
2024-02-28T23:26:27Z
|
Pre-training Differentially Private Models with Limited Public Data
|
The superior performance of large foundation models relies on the use of massive amounts of high-quality data, which often contain sensitive, private and copyrighted material that requires formal protection. While differential privacy (DP) is a prominent method to gauge the degree of security provided to the models, its application is commonly limited to the model fine-tuning stage, due to the performance degradation when applying DP during the pre-training stage. Consequently, DP is yet not capable of protecting a substantial portion of the data used during the initial pre-training process. In this work, we first provide a theoretical understanding of the efficacy of DP training by analyzing the per-iteration loss improvement. We make a key observation that DP optimizers' performance degradation can be significantly mitigated by the use of limited public data, which leads to a novel DP continual pre-training strategy. Empirically, using only 10% of public data, our strategy can achieve DP accuracy of 41.5% on ImageNet-21k (with $epsilon=8$), as well as non-DP accuracy of 55.7% and and 60.0% on downstream tasks Places365 and iNaturalist-2021, respectively, on par with state-of-the-art standard pre-training and substantially outperforming existing DP pre-trained models.
|
[
"['Zhiqi Bu' 'Xinwei Zhang' 'Mingyi Hong' 'Sheng Zha' 'George Karypis']"
] |
null | null |
2402.18759
| null | null |
http://arxiv.org/pdf/2402.18759v2
|
2024-03-06T15:53:46Z
|
2024-02-28T23:57:04Z
|
Learning with Language-Guided State Abstractions
|
We describe a framework for using natural language to design state abstractions for imitation learning. Generalizable policy learning in high-dimensional observation spaces is facilitated by well-designed state representations, which can surface important features of an environment and hide irrelevant ones. These state representations are typically manually specified, or derived from other labor-intensive labeling procedures. Our method, LGA (language-guided abstraction), uses a combination of natural language supervision and background knowledge from language models (LMs) to automatically build state representations tailored to unseen tasks. In LGA, a user first provides a (possibly incomplete) description of a target task in natural language; next, a pre-trained LM translates this task description into a state abstraction function that masks out irrelevant features; finally, an imitation policy is trained using a small number of demonstrations and LGA-generated abstract states. Experiments on simulated robotic tasks show that LGA yields state abstractions similar to those designed by humans, but in a fraction of the time, and that these abstractions improve generalization and robustness in the presence of spurious correlations and ambiguous specifications. We illustrate the utility of the learned abstractions on mobile manipulation tasks with a Spot robot.
|
[
"['Andi Peng' 'Ilia Sucholutsky' 'Belinda Z. Li' 'Theodore R. Sumers'\n 'Thomas L. Griffiths' 'Jacob Andreas' 'Julie A. Shah']"
] |
null | null |
2402.18762
| null | null |
http://arxiv.org/pdf/2402.18762v1
|
2024-02-29T00:02:33Z
|
2024-02-29T00:02:33Z
|
Disentangling the Causes of Plasticity Loss in Neural Networks
|
Underpinning the past decades of work on the design, initialization, and optimization of neural networks is a seemingly innocuous assumption: that the network is trained on a textit{stationary} data distribution. In settings where this assumption is violated, e.g. deep reinforcement learning, learning algorithms become unstable and brittle with respect to hyperparameters and even random seeds. One factor driving this instability is the loss of plasticity, meaning that updating the network's predictions in response to new information becomes more difficult as training progresses. While many recent works provide analyses and partial solutions to this phenomenon, a fundamental question remains unanswered: to what extent do known mechanisms of plasticity loss overlap, and how can mitigation strategies be combined to best maintain the trainability of a network? This paper addresses these questions, showing that loss of plasticity can be decomposed into multiple independent mechanisms and that, while intervening on any single mechanism is insufficient to avoid the loss of plasticity in all cases, intervening on multiple mechanisms in conjunction results in highly robust learning algorithms. We show that a combination of layer normalization and weight decay is highly effective at maintaining plasticity in a variety of synthetic nonstationary learning tasks, and further demonstrate its effectiveness on naturally arising nonstationarities, including reinforcement learning in the Arcade Learning Environment.
|
[
"['Clare Lyle' 'Zeyu Zheng' 'Khimya Khetarpal' 'Hado van Hasselt'\n 'Razvan Pascanu' 'James Martens' 'Will Dabney']"
] |
null | null |
2402.18781
| null | null |
http://arxiv.org/pdf/2402.18781v3
|
2024-03-08T21:36:48Z
|
2024-02-29T01:07:29Z
|
Conjectural Online Learning with First-order Beliefs in Asymmetric
Information Stochastic Games
|
Asymmetric information stochastic games (textsc{aisg}s) arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures. Existing computational methods for textsc{aisg}s are primarily offline and can not adapt to equilibrium deviations. Further, current methods are limited to special classes of textsc{aisg}s to avoid belief hierarchies. To address these limitations, we propose conjectural online learning (textsc{col}), an online method for generic textsc{aisg}s. textsc{col} uses a forecaster-actor-critic (textsc{fac}) architecture where subjective forecasts are used to conjecture the opponents' strategies within a lookahead horizon, and Bayesian learning is used to calibrate the conjectures. To adapt strategies to nonstationary environments, textsc{col} uses online rollout with cost function approximation (actor-critic). We prove that the conjectures produced by textsc{col} are asymptotically consistent with the information feedback in the sense of a relaxed Bayesian consistency. We also prove that the empirical strategy profile induced by textsc{col} converges to the Berk-Nash equilibrium, a solution concept characterizing rationality under subjectivity. Experimental results from an intrusion response use case demonstrate textsc{col}'s superiority over state-of-the-art reinforcement learning methods against nonstationary attacks.
|
[
"['Tao Li' 'Kim Hammar' 'Rolf Stadler' 'Quanyan Zhu']"
] |
null | null |
2402.18787
| null | null |
http://arxiv.org/pdf/2402.18787v1
|
2024-02-29T01:27:38Z
|
2024-02-29T01:27:38Z
|
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial
Defense
|
Recent studies have revealed the vulnerability of Deep Neural Networks (DNNs) to adversarial examples, which can easily fool DNNs into making incorrect predictions. To mitigate this deficiency, we propose a novel adversarial defense method called "Immunity" (Innovative MoE with MUtual information & positioN stabilITY) based on a modified Mixture-of-Experts (MoE) architecture in this work. The key enhancements to the standard MoE are two-fold: 1) integrating of Random Switch Gates (RSGs) to obtain diverse network structures via random permutation of RSG parameters at evaluation time, despite of RSGs being determined after one-time training; 2) devising innovative Mutual Information (MI)-based and Position Stability-based loss functions by capitalizing on Grad-CAM's explanatory power to increase the diversity and the causality of expert networks. Notably, our MI-based loss operates directly on the heatmaps, thereby inducing subtler negative impacts on the classification performance when compared to other losses of the same type, theoretically. Extensive evaluation validates the efficacy of the proposed approach in improving adversarial robustness against a wide range of attacks.
|
[
"['Qiao Han' 'yong huang' 'xinling Guo' 'Yiteng Zhai' 'Yu Qin' 'Yao Yang']"
] |
null | null |
2402.18789
| null | null |
http://arxiv.org/pdf/2402.18789v1
|
2024-02-29T01:33:08Z
|
2024-02-29T01:33:08Z
|
FlexLLM: A System for Co-Serving Large Language Model Inference and
Parameter-Efficient Finetuning
|
Parameter-efficient finetuning (PEFT) is a widely used technique to adapt large language models for different tasks. Service providers typically create separate systems for users to perform PEFT model finetuning and inference tasks. This is because existing systems cannot handle workloads that include a mix of inference and PEFT finetuning requests. As a result, shared GPU resources are underutilized, leading to inefficiencies. To address this problem, we present FlexLLM, the first system that can serve inference and parameter-efficient finetuning requests in the same iteration. Our system leverages the complementary nature of these two tasks and utilizes shared GPU resources to run them jointly, using a method called co-serving. To achieve this, FlexLLM introduces a novel token-level finetuning mechanism, which breaks down the finetuning computation of a sequence into smaller token-level computations and uses dependent parallelization and graph pruning, two static compilation optimizations, to minimize the memory overhead and latency for co-serving. Compared to existing systems, FlexLLM's co-serving approach reduces the activation GPU memory overhead by up to 8x, and the end-to-end GPU memory requirement of finetuning by up to 36% while maintaining a low inference latency and improving finetuning throughput. For example, under a heavy inference workload, FlexLLM can still preserve more than 80% of the peak finetuning throughput, whereas existing systems cannot make any progress with finetuning. The source code of FlexLLM is publicly available at https://github.com/flexflow/FlexFlow.
|
[
"['Xupeng Miao' 'Gabriele Oliaro' 'Xinhao Cheng' 'Mengdi Wu' 'Colin Unger'\n 'Zhihao Jia']"
] |
null | null |
2402.18792
| null | null |
http://arxiv.org/pdf/2402.18792v1
|
2024-02-29T01:49:18Z
|
2024-02-29T01:49:18Z
|
MPAT: Building Robust Deep Neural Networks against Textual Adversarial
Attacks
|
Deep neural networks have been proven to be vulnerable to adversarial examples and various methods have been proposed to defend against adversarial attacks for natural language processing tasks. However, previous defense methods have limitations in maintaining effective defense while ensuring the performance of the original task. In this paper, we propose a malicious perturbation based adversarial training method (MPAT) for building robust deep neural networks against textual adversarial attacks. Specifically, we construct a multi-level malicious example generation strategy to generate adversarial examples with malicious perturbations, which are used instead of original inputs for model training. Additionally, we employ a novel training objective function to ensure achieving the defense goal without compromising the performance on the original task. We conduct comprehensive experiments to evaluate our defense method by attacking five victim models on three benchmark datasets. The result demonstrates that our method is more effective against malicious adversarial attacks compared with previous defense methods while maintaining or further improving the performance on the original task.
|
[
"['Fangyuan Zhang' 'Huichi Zhou' 'Shuangjiao Li' 'Hongtao Wang']"
] |
null | null |
2402.18800
| null | null |
http://arxiv.org/pdf/2402.18800v1
|
2024-02-29T02:13:10Z
|
2024-02-29T02:13:10Z
|
BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise
Missing Data
|
Block-wise missing data poses significant challenges in real-world data imputation tasks. Compared to scattered missing data, block-wise gaps exacerbate adverse effects on subsequent analytic and machine learning tasks, as the lack of local neighboring elements significantly reduces the interpolation capability and predictive power. However, this issue has not received adequate attention. Most SOTA matrix completion methods appeared less effective, primarily due to overreliance on neighboring elements for predictions. We systematically analyze the issue and propose a novel matrix completion method ``BlockEcho" for a more comprehensive solution. This method creatively integrates Matrix Factorization (MF) within Generative Adversarial Networks (GAN) to explicitly retain long-distance inter-element relationships in the original matrix. Besides, we incorporate an additional discriminator for GAN, comparing the generator's intermediate progress with pre-trained MF results to constrain high-order feature distributions. Subsequently, we evaluate BlockEcho on public datasets across three domains. Results demonstrate superior performance over both traditional and SOTA methods when imputing block-wise missing data, especially at higher missing rates. The advantage also holds for scattered missing data at high missing rates. We also contribute on the analyses in providing theoretical justification on the optimality and convergence of fusing MF and GAN for missing block data.
|
[
"['Qiao Han' 'Mingqian Li' 'Yao Yang' 'Yiteng Zhai']"
] |
null | null |
2402.18803
| null | null |
http://arxiv.org/pdf/2402.18803v1
|
2024-02-29T02:16:57Z
|
2024-02-29T02:16:57Z
|
To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair
Training on Shared Models
|
In fair machine learning, one source of performance disparities between groups is over-fitting to groups with relatively few training samples. We derive group-specific bounds on the generalization error of welfare-centric fair machine learning that benefit from the larger sample size of the majority group. We do this by considering group-specific Rademacher averages over a restricted hypothesis class, which contains the family of models likely to perform well with respect to a fair learning objective (e.g., a power-mean). Our simulations demonstrate these bounds improve over a naive method, as expected by theory, with particularly significant improvement for smaller group sizes.
|
[
"['Cyrus Cousins' 'I. Elizabeth Kumar' 'Suresh Venkatasubramanian']"
] |
null | null |
2402.18819
| null | null |
http://arxiv.org/pdf/2402.18819v1
|
2024-02-29T03:06:10Z
|
2024-02-29T03:06:10Z
|
Dual Operating Modes of In-Context Learning
|
In-context learning (ICL) exhibits dual operating modes: task learning, i.e., acquiring a new skill from in-context samples, and task retrieval, i.e., locating and activating a relevant pretrained skill. Recent theoretical work investigates various mathematical models to analyze ICL, but existing models explain only one operating mode at a time. We introduce a probabilistic model, with which one can explain the dual operating modes of ICL simultaneously. Focusing on in-context learning of linear functions, we extend existing models for pretraining data by introducing multiple task groups and task-dependent input distributions. We then analyze the behavior of the optimally pretrained model under the squared loss, i.e., the MMSE estimator of the label given in-context examples. Regarding pretraining task distribution as prior and in-context examples as the observation, we derive the closed-form expression of the task posterior distribution. With the closed-form expression, we obtain a quantitative understanding of the two operating modes of ICL. Furthermore, we shed light on an unexplained phenomenon observed in practice: under certain settings, the ICL risk initially increases and then decreases with more in-context examples. Our model offers a plausible explanation for this "early ascent" phenomenon: a limited number of in-context samples may lead to the retrieval of an incorrect skill, thereby increasing the risk, which will eventually diminish as task learning takes effect with more in-context samples. We also theoretically analyze ICL with biased labels, e.g., zero-shot ICL, where in-context examples are assigned random labels. Lastly, we validate our findings and predictions via experiments involving Transformers and large language models.
|
[
"['Ziqian Lin' 'Kangwook Lee']"
] |
null | null |
2402.18824
| null | null |
http://arxiv.org/pdf/2402.18824v1
|
2024-02-29T03:16:47Z
|
2024-02-29T03:16:47Z
|
Batch size invariant Adam
|
We propose a batch size invariant version of Adam, for use in large-scale, distributed settings, in which the mini-batch is divided into micro-batches which are distributed among worker nodes. For the v term, standard Adam first computes the average over micro-batch gradients, then squares, while in the batch size invariant Adam proposed here, we first square the micro-batch gradients, then average. Previous work (e.g. Malladi et al. 2022) used an alternative approach that involved a square-root scaling of the learning rate, but this approach requires strong assumptions to work; in particular that the gradient variance dominates the square of the expected gradient. In contrast, the approach proposed here gives batch size invariance without this assumption. We confirm that in practice our scheme gives batch size invariance in a much larger range of scenarios than the previous approach.
|
[
"['Xi Wang' 'Laurence Aitchison']"
] |
null | null |
2402.18830
| null | null |
http://arxiv.org/pdf/2402.18830v2
|
2024-03-05T12:39:23Z
|
2024-02-29T03:31:41Z
|
Training-set-free two-stage deep learning for spectroscopic data
de-noising
|
De-noising is a prominent step in the spectra post-processing procedure. Previous machine learning-based methods are fast but mostly based on supervised learning and require a training set that may be typically expensive in real experimental measurements. Unsupervised learning-based algorithms are slow and require many iterations to achieve convergence. Here, we bridge this gap by proposing a training-set-free two-stage deep learning method. We show that the fuzzy fixed input in previous methods can be improved by introducing an adaptive prior. Combined with more advanced optimization techniques, our approach can achieve five times acceleration compared to previous work. Theoretically, we study the landscape of a corresponding non-convex linear problem, and our results indicates that this problem has benign geometry for first-order algorithms to converge.
|
[
"['Dongchen Huang' 'Junde Liu' 'Tian Qian' 'Hongming Weng']"
] |
null | null |
2402.18836
| null | null |
http://arxiv.org/pdf/2402.18836v1
|
2024-02-29T03:53:02Z
|
2024-02-29T03:53:02Z
|
A Model-Based Approach for Improving Reinforcement Learning Efficiency
Leveraging Expert Observations
|
This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations.
|
[
"['Erhan Can Ozcan' 'Vittorio Giammarino' 'James Queeney'\n 'Ioannis Ch. Paschalidis']"
] |
null | null |
2402.18839
| null | null |
http://arxiv.org/pdf/2402.18839v6
|
2024-07-05T12:19:09Z
|
2024-02-29T04:12:32Z
|
Extended Flow Matching: a Method of Conditional Generation with
Generalized Continuity Equation
|
The task of conditional generation is one of the most important applications of generative models, and numerous methods have been developed to date based on the celebrated flow-based models. However, many flow-based models in use today are not built to allow one to introduce an explicit inductive bias to how the conditional distribution to be generated changes with respect to conditions. This can result in unexpected behavior in the task of style transfer, for example. In this research, we introduce extended flow matching (EFM), a direct extension of flow matching that learns a "matrix field" corresponding to the continuous map from the space of conditions to the space of distributions. We show that we can introduce inductive bias to the conditional generation through the matrix field and demonstrate this fact with MMOT-EFM, a version of EFM that aims to minimize the Dirichlet energy or the sensitivity of the distribution with respect to conditions. We will present our theory along with experimental results that support the competitiveness of EFM in conditional generation.
|
[
"['Noboru Isobe' 'Masanori Koyama' 'Jinzhe Zhang' 'Kohei Hayashi'\n 'Kenji Fukumizu']"
] |
null | null |
2402.18846
| null | null |
http://arxiv.org/pdf/2402.18846v2
|
2024-06-24T04:33:30Z
|
2024-02-29T04:40:25Z
|
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
|
Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources. Traditional methods relying on Gaussian processes can hardly scale to high-dimensional data. Deep learning approaches utilize neural network based encoders and decoders to improve scalability. These approaches share encoded representations across fidelities without including corresponding decoder parameters. This hinders inference performance, especially in out-of-distribution scenarios when the highest fidelity data has limited domain coverage. To address these limitations, we propose Multi-fidelity Residual Neural Processes (MFRNP), a novel multi-fidelity surrogate modeling framework. MFRNP explicitly models the residual between the aggregated output from lower fidelities and ground truth at the highest fidelity. The aggregation introduces decoders into the information sharing step and optimizes lower fidelity decoders to accurately capture both in-fidelity and cross-fidelity information. We show that MFRNP significantly outperforms state-of-the-art in learning partial differential equations and a real-world climate modeling task. Our code is published at: https://github.com/Rose-STL-Lab/MFRNP
|
[
"['Ruijia Niu' 'Dongxia Wu' 'Kai Kim' 'Yi-An Ma' 'Duncan Watson-Parris'\n 'Rose Yu']"
] |
null | null |
2402.18851
| null | null |
http://arxiv.org/pdf/2402.18851v1
|
2024-02-29T05:00:01Z
|
2024-02-29T05:00:01Z
|
Applications of 0-1 Neural Networks in Prescription and Prediction
|
A key challenge in medical decision making is learning treatment policies for patients with limited observational data. This challenge is particularly evident in personalized healthcare decision-making, where models need to take into account the intricate relationships between patient characteristics, treatment options, and health outcomes. To address this, we introduce prescriptive networks (PNNs), shallow 0-1 neural networks trained with mixed integer programming that can be used with counterfactual estimation to optimize policies in medium data settings. These models offer greater interpretability than deep neural networks and can encode more complex policies than common models such as decision trees. We show that PNNs can outperform existing methods in both synthetic data experiments and in a case study of assigning treatments for postpartum hypertension. In particular, PNNs are shown to produce policies that could reduce peak blood pressure by 5.47 mm Hg (p=0.02) over existing clinical practice, and by 2 mm Hg (p=0.01) over the next best prescriptive modeling technique. Moreover PNNs were more likely than all other models to correctly identify clinically significant features while existing models relied on potentially dangerous features such as patient insurance information and race that could lead to bias in treatment.
|
[
"['Vrishabh Patil' 'Kara Hoppe' 'Yonatan Mintz']"
] |
null | null |
2402.18853
| null | null |
http://arxiv.org/pdf/2402.18853v1
|
2024-02-29T05:00:30Z
|
2024-02-29T05:00:30Z
|
Rethinking Multi-domain Generalization with A General Learning Objective
|
Multi-domain generalization (mDG) is universally aimed to minimize the discrepancy between training and testing distributions to enhance marginal-to-label distribution mapping. However, existing mDG literature lacks a general learning objective paradigm and often imposes constraints on static target marginal distributions. In this paper, we propose to leverage a $Y$-mapping to relax the constraint. We rethink the learning objective for mDG and design a new textbf{general learning objective} to interpret and analyze most existing mDG wisdom. This general objective is bifurcated into two synergistic amis: learning domain-independent conditional features and maximizing a posterior. Explorations also extend to two effective regularization terms that incorporate prior information and suppress invalid causality, alleviating the issues that come with relaxed constraints. We theoretically contribute an upper bound for the domain alignment of domain-independent conditional features, disclosing that many previous mDG endeavors actually textbf{optimize partially the objective} and thus lead to limited performance. As such, our study distills a general learning objective into four practical components, providing a general, robust, and flexible mechanism to handle complex domain shifts. Extensive empirical results indicate that the proposed objective with $Y$-mapping leads to substantially better mDG performance in various downstream tasks, including regression, segmentation, and classification.
|
[
"['Zhaorui Tan' 'Xi Yang' 'Kaizhu Huang']"
] |
null | null |
2402.18859
| null | null |
http://arxiv.org/abs/2402.18859v2
|
2024-06-08T16:46:51Z
|
2024-02-29T05:17:36Z
|
Taking Second-life Batteries from Exhausted to Empowered using
Experiments, Data Analysis, and Health Estimation
|
The reuse of retired electric vehicle batteries in grid energy storage offers environmental and economic benefits. This study concentrates on health monitoring algorithms for retired batteries deployed in grid storage. Over 15 months of testing, we collect, analyze, and publicize a dataset of second-life batteries, implementing a cycling protocol simulating grid energy storage load profiles within a 3-4 V voltage window. Four machine-learning-based health estimation models, relying on online-accessible features and initial capacity, are compared, with the selected model achieving a mean absolute percentage error below 2.3% on test data. Additionally, an adaptive online health estimation algorithm is proposed by integrating a clustering-based method, thus limiting estimation errors during online deployment. These results showcase the feasibility of repurposing retired batteries for second-life applications. Based on obtained data and power demand, these second-life batteries exhibit potential for over a decade of grid energy storage use.
|
[
"['Xiaofan Cui' 'Muhammad Aadil Khan' 'Gabriele Pozzato' 'Surinder Singh'\n 'Ratnesh Sharma' 'Simona Onori']"
] |
null | null |
2402.18863
| null | null |
http://arxiv.org/pdf/2402.18863v2
|
2024-03-08T02:03:38Z
|
2024-02-29T05:25:23Z
|
Probabilistic Lipschitzness and the Stable Rank for Comparing
Explanation Models
|
Explainability models are now prevalent within machine learning to address the black-box nature of neural networks. The question now is which explainability model is most effective. Probabilistic Lipschitzness has demonstrated that the smoothness of a neural network is fundamentally linked to the quality of post hoc explanations. In this work, we prove theoretical lower bounds on the probabilistic Lipschitzness of Integrated Gradients, LIME and SmoothGrad. We propose a novel metric using probabilistic Lipschitzness, normalised astuteness, to compare the robustness of explainability models. Further, we prove a link between the local Lipschitz constant of a neural network and its stable rank. We then demonstrate that the stable rank of a neural network provides a heuristic for the robustness of explainability models.
|
[
"['Lachlan Simpson' 'Kyle Millar' 'Adriel Cheng' 'Cheng-Chew Lim'\n 'Hong Gunn Chew']"
] |
null | null |
2402.18865
| null | null |
http://arxiv.org/pdf/2402.18865v1
|
2024-02-29T05:27:45Z
|
2024-02-29T05:27:45Z
|
Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient
Tuning
|
Existing research has shown that large language models (LLMs) exhibit remarkable performance in language understanding and generation. However, when LLMs are continuously fine-tuned on complex and diverse domain-specific downstream tasks, the inference performance on historical tasks decreases dramatically, which is known as a catastrophic forgetting problem. A trade-off needs to be kept between learning plasticity and memory stability. Plenty of existing works have explored strategies like memory replay, regularization and parameter isolation, but little is known about the geometric connection of various adjacent minima in the continual LLMs fine-tuning scenarios. In this work, we investigate the geometric connections of different minima through the lens of mode connectivity, which means different minima can be connected by a low-loss valley. Through extensive experiments, we uncover the mode connectivity phenomenon in the LLMs continual learning scenario and find that it can strike a balance between plasticity and stability. Building upon these findings, we propose a simple yet effective method called Interpolation-based LoRA (I-LoRA), which constructs a dual-memory experience replay framework based on LoRA parameter interpolations. Extensive experiments and analysis on eight domain-specific CL benchmarks demonstrate that I-LoRA consistently show significant improvement over the previous state-of-the-art approaches with up to $11%$ performance gains, providing a strong baseline and insights for future research on the large language model continual learning problem. Our code is available at url{https://github.com/which47/LLMCL}.
|
[
"['Weijieying Ren' 'Xinlong Li' 'Lei Wang' 'Tianxiang Zhao' 'Wei Qin']"
] |
null | null |
2402.18866
| null | null |
http://arxiv.org/pdf/2402.18866v2
|
2024-06-04T09:26:15Z
|
2024-02-29T05:34:05Z
|
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
|
Model-based reinforcement learning (MBRL) has been a primary approach to ameliorating the sample efficiency issue as well as to make a generalist agent. However, there has not been much effort toward enhancing the strategy of dreaming itself. Therefore, it is a question whether and how an agent can "dream better" in a more structured and strategic way. In this paper, inspired by the observation from cognitive science suggesting that humans use a spatial divide-and-conquer strategy in planning, we propose a new MBRL agent, called Dr. Strategy, which is equipped with a novel Dreaming Strategy. The proposed agent realizes a version of divide-and-conquer-like strategy in dreaming. This is achieved by learning a set of latent landmarks and then utilizing these to learn a landmark-conditioned highway policy. With the highway policy, the agent can first learn in the dream to move to a landmark, and from there it tackles the exploration and achievement task in a more focused way. In experiments, we show that the proposed model outperforms prior pixel-based MBRL methods in various visually complex and partially observable navigation tasks.
|
[
"['Hany Hamed' 'Subin Kim' 'Dongyeong Kim' 'Jaesik Yoon' 'Sungjin Ahn']"
] |
null | null |
2402.18875
| null | null |
http://arxiv.org/pdf/2402.18875v1
|
2024-02-29T05:44:41Z
|
2024-02-29T05:44:41Z
|
Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks
|
Heterogeneous Graph Neural Networks (HGNNs) are a class of deep learning models designed specifically for heterogeneous graphs, which are graphs that contain different types of nodes and edges. This paper investigates the application of curriculum learning techniques to improve the performance and robustness of Heterogeneous Graph Neural Networks (GNNs). To better classify the quality of the data, we design a loss-aware training schedule, named LTS that measures the quality of every nodes of the data and incorporate the training dataset into the model in a progressive manner that increases difficulty step by step. LTS can be seamlessly integrated into various frameworks, effectively reducing bias and variance, mitigating the impact of noisy data, and enhancing overall accuracy. Our findings demonstrate the efficacy of curriculum learning in enhancing HGNNs capabilities for analyzing complex graph-structured data. The code is public at https://github.com/LARS-research/CLGNN/.
|
[
"['Zhen Hao Wong' 'Hansi Yang' 'Xiaoyi Fu' 'Quanming Yao']"
] |
null | null |
2402.18884
| null | null |
http://arxiv.org/pdf/2402.18884v1
|
2024-02-29T06:02:45Z
|
2024-02-29T06:02:45Z
|
Supervised Contrastive Representation Learning: Landscape Analysis with
Unconstrained Features
|
Recent findings reveal that over-parameterized deep neural networks, trained beyond zero training-error, exhibit a distinctive structural pattern at the final layer, termed as Neural-collapse (NC). These results indicate that the final hidden-layer outputs in such networks display minimal within-class variations over the training set. While existing research extensively investigates this phenomenon under cross-entropy loss, there are fewer studies focusing on its contrastive counterpart, supervised contrastive (SC) loss. Through the lens of NC, this paper employs an analytical approach to study the solutions derived from optimizing the SC loss. We adopt the unconstrained features model (UFM) as a representative proxy for unveiling NC-related phenomena in sufficiently over-parameterized deep networks. We show that, despite the non-convexity of SC loss minimization, all local minima are global minima. Furthermore, the minimizer is unique (up to a rotation). We prove our results by formalizing a tight convex relaxation of the UFM. Finally, through this convex formulation, we delve deeper into characterizing the properties of global solutions under label-imbalanced training data.
|
[
"['Tina Behnia' 'Christos Thrampoulidis']"
] |
null | null |
2402.18886
| null | null |
http://arxiv.org/pdf/2402.18886v1
|
2024-02-29T06:11:21Z
|
2024-02-29T06:11:21Z
|
BP-DeepONet: A new method for cuffless blood pressure estimation using
the physcis-informed DeepONet
|
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, with blood pressure serving as a crucial indicator. Arterial blood pressure (ABP) waveforms provide continuous pressure measurements throughout the cardiac cycle and offer valuable diagnostic insights. Consequently, there is a significant demand for non-invasive and cuff-less methods to measure ABP waveforms continuously. Accurate prediction of ABP waveforms can also improve the estimation of mean blood pressure, an essential cardiovascular health characteristic. This study proposes a novel framework based on the physics-informed DeepONet approach to predict ABP waveforms. Unlike previous methods, our approach requires the predicted ABP waveforms to satisfy the Navier-Stokes equation with a time-periodic condition and a Windkessel boundary condition. Notably, our framework is the first to predict ABP waveforms continuously, both with location and time, within the part of the artery that is being simulated. Furthermore, our method only requires ground truth data at the outlet boundary and can handle periodic conditions with varying periods. Incorporating the Windkessel boundary condition in our solution allows for generating natural physical reflection waves, which closely resemble measurements observed in real-world cases. Moreover, accurately estimating the hyper-parameters in the Navier-Stokes equation for our simulations poses a significant challenge. To overcome this obstacle, we introduce the concept of meta-learning, enabling the neural networks to learn these parameters during the training process.
|
[
"['Lingfeng Li' 'Xue-Cheng Tai' 'Raymond Chan']"
] |
null | null |
2402.18888
| null | null |
http://arxiv.org/pdf/2402.18888v2
|
2024-03-01T15:38:15Z
|
2024-02-29T06:13:10Z
|
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in
Heterogeneous Data Silos
|
Federated learning (FL), aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos. While previous studies have explored discrete representations to enhance model generalization across minor distributional shifts, these approaches often struggle to adapt to new data silos with significantly divergent distributions. In response, we have identified that models derived from FL exhibit markedly increased uncertainty when applied to data silos with unfamiliar distributions. Consequently, we propose an innovative yet straightforward iterative framework, termed Uncertainty-Based Extensible-Codebook Federated Learning (UEFL). This framework dynamically maps latent features to trainable discrete vectors, assesses the uncertainty, and specifically extends the discretization dictionary or codebook for silos exhibiting high uncertainty. Our approach aims to simultaneously enhance accuracy and reduce uncertainty by explicitly addressing the diversity of data distributions, all while maintaining minimal computational overhead in environments characterized by heterogeneous data silos. Through experiments conducted on five datasets, our method has demonstrated its superiority, achieving significant improvements in accuracy (by 3%--22.1%) and uncertainty reduction (by 38.83%--96.24%), thereby outperforming contemporary state-of-the-art methods. The source code is available at https://github.com/destiny301/uefl.
|
[
"['Tianyi Zhang' 'Yu Cao' 'Dianbo Liu']"
] |
null | null |
2402.18905
| null | null |
http://arxiv.org/pdf/2402.18905v1
|
2024-02-29T07:01:48Z
|
2024-02-29T07:01:48Z
|
On the Convergence of Differentially-Private Fine-tuning: To Linearly
Probe or to Fully Fine-tune?
|
Differentially private (DP) machine learning pipelines typically involve a two-phase process: non-private pre-training on a public dataset, followed by fine-tuning on private data using DP optimization techniques. In the DP setting, it has been observed that full fine-tuning may not always yield the best test accuracy, even for in-distribution data. This paper (1) analyzes the training dynamics of DP linear probing (LP) and full fine-tuning (FT), and (2) explores the phenomenon of sequential fine-tuning, starting with linear probing and transitioning to full fine-tuning (LP-FT), and its impact on test loss. We provide theoretical insights into the convergence of DP fine-tuning within an overparameterized neural network and establish a utility curve that determines the allocation of privacy budget between linear probing and full fine-tuning. The theoretical results are supported by empirical evaluations on various benchmarks and models. The findings reveal the complex nature of DP fine-tuning methods. These results contribute to a deeper understanding of DP machine learning and highlight the importance of considering the allocation of privacy budget in the fine-tuning process.
|
[
"['Shuqi Ke' 'Charlie Hou' 'Giulia Fanti' 'Sewoong Oh']"
] |
null | null |
2402.18910
| null | null |
http://arxiv.org/pdf/2402.18910v1
|
2024-02-29T07:09:01Z
|
2024-02-29T07:09:01Z
|
DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
|
Causality has been combined with machine learning to produce robust representations for domain generalization. Most existing methods of this type require massive data from multiple domains to identify causal features by cross-domain variations, which can be expensive or even infeasible and may lead to misidentification in some cases. In this work, we make a different attempt by leveraging the demonstration data distribution to discover the causal features for a domain generalizable policy. We design a novel framework, called DIGIC, to identify the causal features by finding the direct cause of the expert action from the demonstration data distribution via causal discovery. Our framework can achieve domain generalizable imitation learning with only single-domain data and serve as a complement for cross-domain variation-based methods under non-structural assumptions on the underlying causal models. Our empirical study in various control tasks shows that the proposed framework evidently improves the domain generalization performance and has comparable performance to the expert in the original domain simultaneously.
|
[
"['Yang Chen' 'Yitao Liang' 'Zhouchen Lin']"
] |
null | null |
2402.18917
| null | null |
http://arxiv.org/pdf/2402.18917v1
|
2024-02-29T07:17:04Z
|
2024-02-29T07:17:04Z
|
Stop Relying on No-Choice and Do not Repeat the Moves: Optimal,
Efficient and Practical Algorithms for Assortment Optimization
|
We address the problem of active online assortment optimization problem with preference feedback, which is a framework for modeling user choices and subsetwise utility maximization. The framework is useful in various real-world applications including ad placement, online retail, recommender systems, fine-tuning language models, amongst many. The problem, although has been studied in the past, lacks an intuitive and practical solution approach with simultaneously efficient algorithm and optimal regret guarantee. E.g., popularly used assortment selection algorithms often require the presence of a `strong reference' which is always included in the choice sets, further they are also designed to offer the same assortments repeatedly until the reference item gets selected -- all such requirements are quite unrealistic for practical applications. In this paper, we designed efficient algorithms for the problem of regret minimization in assortment selection with emph{Plackett Luce} (PL) based user choices. We designed a novel concentration guarantee for estimating the score parameters of the PL model using `emph{Pairwise Rank-Breaking}', which builds the foundation of our proposed algorithms. Moreover, our methods are practical, provably optimal, and devoid of the aforementioned limitations of the existing methods. Empirical evaluations corroborate our findings and outperform the existing baselines.
|
[
"['Aadirupa Saha' 'Pierre Gaillard']"
] |
null | null |
2402.18919
| null | null |
http://arxiv.org/pdf/2402.18919v2
|
2024-03-02T14:57:12Z
|
2024-02-29T07:24:24Z
|
Decompose-and-Compose: A Compositional Approach to Mitigating Spurious
Correlation
|
While standard Empirical Risk Minimization (ERM) training is proven effective for image classification on in-distribution data, it fails to perform well on out-of-distribution samples. One of the main sources of distribution shift for image classification is the compositional nature of images. Specifically, in addition to the main object or component(s) determining the label, some other image components usually exist, which may lead to the shift of input distribution between train and test environments. More importantly, these components may have spurious correlations with the label. To address this issue, we propose Decompose-and-Compose (DaC), which improves robustness to correlation shift by a compositional approach based on combining elements of images. Based on our observations, models trained with ERM usually highly attend to either the causal components or the components having a high spurious correlation with the label (especially in datapoints on which models have a high confidence). In fact, according to the amount of spurious correlation and the easiness of classification based on the causal or non-causal components, the model usually attends to one of these more (on samples with high confidence). Following this, we first try to identify the causal components of images using class activation maps of models trained with ERM. Afterward, we intervene on images by combining them and retraining the model on the augmented data, including the counterfactual ones. Along with its high interpretability, this work proposes a group-balancing method by intervening on images without requiring group labels or information regarding the spurious features during training. The method has an overall better worst group accuracy compared to previous methods with the same amount of supervision on the group labels in correlation shift.
|
[
"['Fahimeh Hosseini Noohdani' 'Parsa Hosseini' 'Aryan Yazdan Parast'\n 'Hamidreza Yaghoubi Araghi' 'Mahdieh Soleymani Baghshah']"
] |
null | null |
2402.18946
| null | null |
http://arxiv.org/pdf/2402.18946v2
|
2024-03-05T09:00:29Z
|
2024-02-29T08:25:32Z
|
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in
High-Order Uncertain Models
|
This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorithm, thus enhancing its adaptability. Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model's online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from new samples, in conjunction with the learned hyperparameters. In the second phase, we propose a safety filter based on high-order control barrier functions (HOCBFs), synergized with the previously trained learning model. By leveraging the compound kernel from the first phase, we effectively address the inherent limitations of GPs in handling high-dimensional problems for real-time applications. The derived controller ensures a rigorous lower bound on the probability of satisfying the safety specification. Finally, the efficacy of our proposed algorithm is demonstrated through real-time obstacle avoidance experiments executed using both a simulation platform and a real-world 7-DOF robot.
|
[
"['Yu Zhang' 'Long Wen' 'Xiangtong Yao' 'Zhenshan Bing' 'Linghuan Kong'\n 'Wei He' 'Alois Knoll']"
] |
null | null |
2402.18949
| null | null |
http://arxiv.org/pdf/2402.18949v1
|
2024-02-29T08:27:01Z
|
2024-02-29T08:27:01Z
|
Improving Group Connectivity for Generalization of Federated Deep
Learning
|
Federated learning (FL) involves multiple heterogeneous clients collaboratively training a global model via iterative local updates and model fusion. The generalization of FL's global model has a large gap compared with centralized training, which is its bottleneck for broader applications. In this paper, we study and improve FL's generalization through a fundamental ``connectivity'' perspective, which means how the local models are connected in the parameter region and fused into a generalized global model. The term ``connectivity'' is derived from linear mode connectivity (LMC), studying the interpolated loss landscape of two different solutions (e.g., modes) of neural networks. Bridging the gap between LMC and FL, in this paper, we leverage fixed anchor models to empirically and theoretically study the transitivity property of connectivity from two models (LMC) to a group of models (model fusion in FL). Based on the findings, we propose FedGuCci and FedGuCci+, improving group connectivity for better generalization. It is shown that our methods can boost the generalization of FL under client heterogeneity across various tasks (4 CV datasets and 6 NLP datasets), models (both convolutional and transformer-based), and training paradigms (both from-scratch and pretrain-finetune).
|
[
"['Zexi Li' 'Jie Lin' 'Zhiqi Li' 'Didi Zhu' 'Chao Wu']"
] |
null | null |
2402.18974
| null | null |
http://arxiv.org/pdf/2402.18974v1
|
2024-02-29T09:26:46Z
|
2024-02-29T09:26:46Z
|
Graph Generation via Spectral Diffusion
|
In this paper, we present GRASP, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process. Specifically, we propose to use a denoising model to sample eigenvectors and eigenvalues from which we can reconstruct the graph Laplacian and adjacency matrix. Our permutation invariant model can also handle node features by concatenating them to the eigenvectors of each node. Using the Laplacian spectrum allows us to naturally capture the structural characteristics of the graph and work directly in the node space while avoiding the quadratic complexity bottleneck that limits the applicability of other methods. This is achieved by truncating the spectrum, which as we show in our experiments results in a faster yet accurate generative process. An extensive set of experiments on both synthetic and real world graphs demonstrates the strengths of our model against state-of-the-art alternatives.
|
[
"['Giorgia Minello' 'Alessandro Bicciato' 'Luca Rossi' 'Andrea Torsello'\n 'Luca Cosmo']"
] |
null | null |
2402.18994
| null | null |
http://arxiv.org/pdf/2402.18994v1
|
2024-02-29T09:46:44Z
|
2024-02-29T09:46:44Z
|
Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural
Networks
|
As the role of artificial intelligence becomes increasingly pivotal in modern society, the efficient training and deployment of deep neural networks have emerged as critical areas of focus. Recent advancements in attention-based large neural architectures have spurred the development of AI accelerators, facilitating the training of extensive, multi-billion parameter models. Despite their effectiveness, these powerful networks often incur high execution costs in production environments. Neuromorphic computing, inspired by biological neural processes, offers a promising alternative. By utilizing temporally-sparse computations, Spiking Neural Networks (SNNs) offer to enhance energy efficiency through a reduced and low-power hardware footprint. However, the training of SNNs can be challenging due to their recurrent nature which cannot as easily leverage the massive parallelism of modern AI accelerators. To facilitate the investigation of SNN architectures and dynamics researchers have sought to bridge Python-based deep learning frameworks such as PyTorch or TensorFlow with custom-implemented compute kernels. This paper introduces Spyx, a new and lightweight SNN simulation and optimization library designed in JAX. By pre-staging data in the expansive vRAM of contemporary accelerators and employing extensive JIT compilation, Spyx allows for SNN optimization to be executed as a unified, low-level program on NVIDIA GPUs or Google TPUs. This approach achieves optimal hardware utilization, surpassing the performance of many existing SNN training frameworks while maintaining considerable flexibility.
|
[
"['Kade M. Heckel' 'Thomas Nowotny']"
] |
null | null |
2402.18995
| null | null |
http://arxiv.org/pdf/2402.18995v1
|
2024-02-29T09:46:47Z
|
2024-02-29T09:46:47Z
|
Negative-Binomial Randomized Gamma Markov Processes for Heterogeneous
Overdispersed Count Time Series
|
Modeling count-valued time series has been receiving increasing attention since count time series naturally arise in physical and social domains. Poisson gamma dynamical systems (PGDSs) are newly-developed methods, which can well capture the expressive latent transition structure and bursty dynamics behind count sequences. In particular, PGDSs demonstrate superior performance in terms of data imputation and prediction, compared with canonical linear dynamical system (LDS) based methods. Despite these advantages, PGDS cannot capture the heterogeneous overdispersed behaviours of the underlying dynamic processes. To mitigate this defect, we propose a negative-binomial-randomized gamma Markov process, which not only significantly improves the predictive performance of the proposed dynamical system, but also facilitates the fast convergence of the inference algorithm. Moreover, we develop methods to estimate both factor-structured and graph-structured transition dynamics, which enable us to infer more explainable latent structure, compared with PGDSs. Finally, we demonstrate the explainable latent structure learned by the proposed method, and show its superior performance in imputing missing data and forecasting future observations, compared with the related models.
|
[
"['Rui Huang' 'Sikun Yang' 'Heinz Koeppl']"
] |
null | null |
2402.19009
| null | null |
http://arxiv.org/pdf/2402.19009v2
|
2024-06-05T07:28:52Z
|
2024-02-29T10:08:57Z
|
Unified Generation, Reconstruction, and Representation: Generalized
Diffusion with Adaptive Latent Encoding-Decoding
|
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein sequences and continuous images. Existing model families, like variational autoencoders (VAEs), generative adversarial networks (GANs), autoregressive models, and (latent) diffusion models, generally excel in specific capabilities and data types but fall short in others. We introduce Generalized Encoding-Decoding Diffusion Probabilistic Models (EDDPMs) which integrate the core capabilities for broad applicability and enhanced performance. EDDPMs generalize the Gaussian noising-denoising in standard diffusion by introducing parameterized encoding-decoding. Crucially, EDDPMs are compatible with the well-established diffusion model objective and training recipes, allowing effective learning of the encoder-decoder parameters jointly with diffusion. By choosing appropriate encoder/decoder (e.g., large language models), EDDPMs naturally apply to different data types. Extensive experiments on text, proteins, and images demonstrate the flexibility to handle diverse data and tasks and the strong improvement over various existing models.
|
[
"['Guangyi Liu' 'Yu Wang' 'Zeyu Feng' 'Qiyu Wu' 'Liping Tang' 'Yuan Gao'\n 'Zhen Li' 'Shuguang Cui' 'Julian McAuley' 'Zichao Yang' 'Eric P. Xing'\n 'Zhiting Hu']"
] |
null | null |
2402.19016
| null | null |
http://arxiv.org/pdf/2402.19016v1
|
2024-02-29T10:23:23Z
|
2024-02-29T10:23:23Z
|
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for
Sparse Basis Recovery
|
Sparse basis recovery is a classical and important statistical learning problem when the number of model dimensions $p$ is much larger than the number of samples $n$. However, there has been little work that studies sparse basis recovery in the Federated Learning (FL) setting, where the client data's differential privacy (DP) must also be simultaneously protected. In particular, the performance guarantees of existing DP-FL algorithms (such as DP-SGD) will degrade significantly when $p gg n$, and thus, they will fail to learn the true underlying sparse model accurately. In this work, we develop a new differentially private sparse basis recovery algorithm for the FL setting, called SPriFed-OMP. SPriFed-OMP converts OMP (Orthogonal Matching Pursuit) to the FL setting. Further, it combines SMPC (secure multi-party computation) and DP to ensure that only a small amount of noise needs to be added in order to achieve differential privacy. As a result, SPriFed-OMP can efficiently recover the true sparse basis for a linear model with only $n = O(sqrt{p})$ samples. We further present an enhanced version of our approach, SPriFed-OMP-GRAD based on gradient privatization, that improves the performance of SPriFed-OMP. Our theoretical analysis and empirical results demonstrate that both SPriFed-OMP and SPriFed-OMP-GRAD terminate in a small number of steps, and they significantly outperform the previous state-of-the-art DP-FL solutions in terms of the accuracy-privacy trade-off.
|
[
"['Ajinkya Kiran Mulay' 'Xiaojun Lin']"
] |
null | null |
2402.19025
| null | null |
http://arxiv.org/pdf/2402.19025v1
|
2024-02-29T10:37:40Z
|
2024-02-29T10:37:40Z
|
Combination of Weak Learners eXplanations to Improve Random Forest
eXplicability Robustness
|
The notion of robustness in XAI refers to the observed variations in the explanation of the prediction of a learned model with respect to changes in the input leading to that prediction. Intuitively, if the input being explained is modified slightly subtly enough so as to not change the prediction of the model too much, then we would expect that the explanation provided for that new input does not change much either. We argue that a combination through discriminative averaging of ensembles weak learners explanations can improve the robustness of explanations in ensemble methods.This approach has been implemented and tested with post-hoc SHAP method and Random Forest ensemble with successful results. The improvements obtained have been measured quantitatively and some insights into the explicability robustness in ensemble methods are presented.
|
[
"['Riccardo Pala' 'Esteban García-Cuesta']"
] |
null | null |
2402.19037
| null | null |
http://arxiv.org/pdf/2402.19037v1
|
2024-02-29T11:02:47Z
|
2024-02-29T11:02:47Z
|
A Deep-Learning Technique to Locate Cryptographic Operations in
Side-Channel Traces
|
Side-channel attacks allow extracting secret information from the execution of cryptographic primitives by correlating the partially known computed data and the measured side-channel signal. However, to set up a successful side-channel attack, the attacker has to perform i) the challenging task of locating the time instant in which the target cryptographic primitive is executed inside a side-channel trace and then ii)the time-alignment of the measured data on that time instant. This paper presents a novel deep-learning technique to locate the time instant in which the target computed cryptographic operations are executed in the side-channel trace. In contrast to state-of-the-art solutions, the proposed methodology works even in the presence of trace deformations obtained through random delay insertion techniques. We validated our proposal through a successful attack against a variety of unprotected and protected cryptographic primitives that have been executed on an FPGA-implemented system-on-chip featuring a RISC-V CPU.
|
[
"['Giuseppe Chiari' 'Davide Galli' 'Francesco Lattari' 'Matteo Matteucci'\n 'Davide Zoni']"
] |
null | null |
2402.19047
| null | null |
http://arxiv.org/pdf/2402.19047v2
|
2024-03-04T11:09:49Z
|
2024-02-29T11:20:16Z
|
Theoretical Foundations of Deep Selective State-Space Models
|
Structured state-space models (SSMs) such as S4, stemming from the seminal work of Gu et al., are gaining popularity as effective approaches for modeling sequential data. Deep SSMs demonstrate outstanding performance across a diverse set of domains, at a reduced training and inference cost compared to attention-based transformers. Recent developments show that if the linear recurrence powering SSMs allows for multiplicative interactions between inputs and hidden states (e.g. GateLoop, Mamba, GLA), then the resulting architecture can surpass in both in accuracy and efficiency attention-powered foundation models trained on text, at scales of billion parameters. In this paper, we give theoretical grounding to this recent finding using tools from Rough Path Theory: we show that when random linear recurrences are equipped with simple input-controlled transitions (selectivity mechanism), then the hidden state is provably a low-dimensional projection of a powerful mathematical object called the signature of the input -- capturing non-linear interactions between tokens at distinct timescales. Our theory not only motivates the success of modern selective state-space models such as Mamba but also provides a solid framework to understand the expressive power of future SSM variants.
|
[
"['Nicola Muca Cirone' 'Antonio Orvieto' 'Benjamin Walker'\n 'Cristopher Salvi' 'Terry Lyons']"
] |
null | null |
2402.19062
| null | null |
http://arxiv.org/abs/2402.19062v2
|
2024-03-01T08:54:53Z
|
2024-02-29T11:45:24Z
|
Graph Convolutional Neural Networks for Automated Echocardiography View
Recognition: A Holistic Approach
|
To facilitate diagnosis on cardiac ultrasound (US), clinical practice has established several standard views of the heart, which serve as reference points for diagnostic measurements and define viewports from which images are acquired. Automatic view recognition involves grouping those images into classes of standard views. Although deep learning techniques have been successful in achieving this, they still struggle with fully verifying the suitability of an image for specific measurements due to factors like the correct location, pose, and potential occlusions of cardiac structures. Our approach goes beyond view classification and incorporates a 3D mesh reconstruction of the heart that enables several more downstream tasks, like segmentation and pose estimation. In this work, we explore learning 3D heart meshes via graph convolutions, using similar techniques to learn 3D meshes in natural images, such as human pose estimation. As the availability of fully annotated 3D images is limited, we generate synthetic US images from 3D meshes by training an adversarial denoising diffusion model. Experiments were conducted on synthetic and clinical cases for view recognition and structure detection. The approach yielded good performance on synthetic images and, despite being exclusively trained on synthetic data, it already showed potential when applied to clinical images. With this proof-of-concept, we aim to demonstrate the benefits of graphs to improve cardiac view recognition that can ultimately lead to better efficiency in cardiac diagnosis.
|
[
"['Sarina Thomas' 'Cristiana Tiago' 'Børge Solli Andreassen'\n 'Svein Arne Aase' 'Jurica Šprem' 'Erik Steen' 'Anne Solberg'\n 'Guy Ben-Yosef']"
] |
null | null |
2402.19072
| null | null |
http://arxiv.org/pdf/2402.19072v1
|
2024-02-29T11:54:35Z
|
2024-02-29T11:54:35Z
|
TimeXer: Empowering Transformers for Time Series Forecasting with
Exogenous Variables
|
Recent studies have demonstrated remarkable performance in time series forecasting. However, due to the partially-observed nature of real-world applications, solely focusing on the target of interest, so-called endogenous variables, is usually insufficient to guarantee accurate forecasting. Notably, a system is often recorded into multiple variables, where the exogenous series can provide valuable external information for endogenous variables. Thus, unlike prior well-established multivariate or univariate forecasting that either treats all the variables equally or overlooks exogenous information, this paper focuses on a practical setting, which is time series forecasting with exogenous variables. We propose a novel framework, TimeXer, to utilize external information to enhance the forecasting of endogenous variables. With a deftly designed embedding layer, TimeXer empowers the canonical Transformer architecture with the ability to reconcile endogenous and exogenous information, where patch-wise self-attention and variate-wise cross-attention are employed. Moreover, a global endogenous variate token is adopted to effectively bridge the exogenous series into endogenous temporal patches. Experimentally, TimeXer significantly improves time series forecasting with exogenous variables and achieves consistent state-of-the-art performance in twelve real-world forecasting benchmarks.
|
[
"['Yuxuan Wang' 'Haixu Wu' 'Jiaxiang Dong' 'Yong Liu' 'Yunzhong Qiu'\n 'Haoran Zhang' 'Jianmin Wang' 'Mingsheng Long']"
] |
null | null |
2402.19078
| null | null |
http://arxiv.org/pdf/2402.19078v2
|
2024-03-14T14:47:54Z
|
2024-02-29T12:03:05Z
|
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
|
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution. In the past few decades, numerous methods have been proposed to find Pareto solutions that represent different optimal trade-offs among the objectives for a given problem. However, these existing methods could have high computational complexity or may not have good theoretical properties for solving a general differentiable multi-objective optimization problem. In this work, by leveraging the smooth optimization technique, we propose a novel and lightweight smooth Tchebycheff scalarization approach for gradient-based multi-objective optimization. It has good theoretical properties for finding all Pareto solutions with valid trade-off preferences, while enjoying significantly lower computational complexity compared to other methods. Experimental results on various real-world application problems fully demonstrate the effectiveness of our proposed method.
|
[
"['Xi Lin' 'Xiaoyuan Zhang' 'Zhiyuan Yang' 'Fei Liu' 'Zhenkun Wang'\n 'Qingfu Zhang']"
] |
null | null |
2402.19090
| null | null |
http://arxiv.org/pdf/2402.19090v1
|
2024-02-29T12:17:54Z
|
2024-02-29T12:17:54Z
|
Best Arm Identification with Resource Constraints
|
Motivated by the cost heterogeneity in experimentation across different alternatives, we study the Best Arm Identification with Resource Constraints (BAIwRC) problem. The agent aims to identify the best arm under resource constraints, where resources are consumed for each arm pull. We make two novel contributions. We design and analyze the Successive Halving with Resource Rationing algorithm (SH-RR). The SH-RR achieves a near-optimal non-asymptotic rate of convergence in terms of the probability of successively identifying an optimal arm. Interestingly, we identify a difference in convergence rates between the cases of deterministic and stochastic resource consumption.
|
[
"['Zitian Li' 'Wang Chi Cheung']"
] |
null | null |
2402.19095
| null | null |
http://arxiv.org/pdf/2402.19095v2
|
2024-03-08T05:30:10Z
|
2024-02-29T12:24:20Z
|
A Protein Structure Prediction Approach Leveraging Transformer and CNN
Integration
|
Proteins are essential for life, and their structure determines their function. The protein secondary structure is formed by the folding of the protein primary structure, and the protein tertiary structure is formed by the bending and folding of the secondary structure. Therefore, the study of protein secondary structure is very helpful to the overall understanding of protein structure. Although the accuracy of protein secondary structure prediction has continuously improved with the development of machine learning and deep learning, progress in the field of protein structure prediction, unfortunately, remains insufficient to meet the large demand for protein information. Therefore, based on the advantages of deep learning-based methods in feature extraction and learning ability, this paper adopts a two-dimensional fusion deep neural network model, DstruCCN, which uses Convolutional Neural Networks (CCN) and a supervised Transformer protein language model for single-sequence protein structure prediction. The training features of the two are combined to predict the protein Transformer binding site matrix, and then the three-dimensional structure is reconstructed using energy minimization.
|
[
"['Yanlin Zhou' 'Kai Tan' 'Xinyu Shen' 'Zheng He' 'Haotian Zheng']"
] |
null | null |
2402.19101
| null | null |
http://arxiv.org/pdf/2402.19101v1
|
2024-02-29T12:29:58Z
|
2024-02-29T12:29:58Z
|
Effective Two-Stage Knowledge Transfer for Multi-Entity Cross-Domain
Recommendation
|
In recent years, the recommendation content on e-commerce platforms has become increasingly rich -- a single user feed may contain multiple entities, such as selling products, short videos, and content posts. To deal with the multi-entity recommendation problem, an intuitive solution is to adopt the shared-network-based architecture for joint training. The idea is to transfer the extracted knowledge from one type of entity (source entity) to another (target entity). However, different from the conventional same-entity cross-domain recommendation, multi-entity knowledge transfer encounters several important issues: (1) data distributions of the source entity and target entity are naturally different, making the shared-network-based joint training susceptible to the negative transfer issue, (2) more importantly, the corresponding feature schema of each entity is not exactly aligned (e.g., price is an essential feature for selling product while missing for content posts), making the existing methods no longer appropriate. Recent researchers have also experimented with the pre-training and fine-tuning paradigm. Again, they only consider the scenarios with the same entity type and feature systems, which is inappropriate in our case. To this end, we design a pre-training & fine-tuning based Multi-entity Knowledge Transfer framework called MKT. MKT utilizes a multi-entity pre-training module to extract transferable knowledge across different entities. In particular, a feature alignment module is first applied to scale and align different feature schemas. Afterward, a couple of knowledge extractors are employed to extract the common and entity-specific knowledge. In the end, the extracted common knowledge is adopted for target entity model training. Through extensive offline and online experiments, we demonstrated the superiority of MKT over multiple State-Of-The-Art methods.
|
[
"['Jianyu Guan' 'Zongming Yin' 'Tianyi Zhang' 'Leihui Chen' 'Yin Zhang'\n 'Fei Huang' 'Jufeng Chen' 'Shuguang Han']"
] |
null | null |
2402.19102
| null | null |
http://arxiv.org/pdf/2402.19102v1
|
2024-02-29T12:33:14Z
|
2024-02-29T12:33:14Z
|
FlatNAS: optimizing Flatness in Neural Architecture Search for
Out-of-Distribution Robustness
|
Neural Architecture Search (NAS) paves the way for the automatic definition of Neural Network (NN) architectures, attracting increasing research attention and offering solutions in various scenarios. This study introduces a novel NAS solution, called Flat Neural Architecture Search (FlatNAS), which explores the interplay between a novel figure of merit based on robustness to weight perturbations and single NN optimization with Sharpness-Aware Minimization (SAM). FlatNAS is the first work in the literature to systematically explore flat regions in the loss landscape of NNs in a NAS procedure, while jointly optimizing their performance on in-distribution data, their out-of-distribution (OOD) robustness, and constraining the number of parameters in their architecture. Differently from current studies primarily concentrating on OOD algorithms, FlatNAS successfully evaluates the impact of NN architectures on OOD robustness, a crucial aspect in real-world applications of machine and deep learning. FlatNAS achieves a good trade-off between performance, OOD generalization, and the number of parameters, by using only in-distribution data in the NAS exploration. The OOD robustness of the NAS-designed models is evaluated by focusing on robustness to input data corruptions, using popular benchmark datasets in the literature.
|
[
"['Matteo Gambella' 'Fabrizio Pittorino' 'Manuel Roveri']"
] |
null | null |
2402.19105
| null | null |
http://arxiv.org/pdf/2402.19105v1
|
2024-02-29T12:36:10Z
|
2024-02-29T12:36:10Z
|
CollaFuse: Navigating Limited Resources and Privacy in Collaborative
Generative AI
|
In the landscape of generative artificial intelligence, diffusion-based models present challenges for socio-technical systems in data requirements and privacy. Traditional approaches like federated learning distribute the learning process but strain individual clients, especially with constrained resources (e.g., edge devices). In response to these challenges, we introduce CollaFuse, a novel framework inspired by split learning. Tailored for efficient and collaborative use of denoising diffusion probabilistic models, CollaFuse enables shared server training and inference, alleviating client computational burdens. This is achieved by retaining data and computationally inexpensive GPU processes locally at each client while outsourcing the computationally expensive processes to the shared server. Demonstrated in a healthcare context, CollaFuse enhances privacy by highly reducing the need for sensitive information sharing. These capabilities hold the potential to impact various application areas, such as the design of edge computing solutions, healthcare research, or autonomous driving. In essence, our work advances distributed machine learning, shaping the future of collaborative GenAI networks.
|
[
"['Domenique Zipperling' 'Simeon Allmendinger' 'Lukas Struppek'\n 'Niklas Kühl']"
] |
null | null |
2402.19110
| null | null |
http://arxiv.org/pdf/2402.19110v1
|
2024-02-29T12:41:54Z
|
2024-02-29T12:41:54Z
|
Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in
Energy and Contingency Reserve Markets
|
The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks various revenue streams by taking part in multiple markets to unlock its full potential, but effective algorithms for joint-market participation under price uncertainties are insufficiently explored in the existing research. To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets. Our approach leverages a transformer-based temporal feature extractor to effectively respond to price fluctuations in seven markets simultaneously and helps DRL learn the best BESS bidding strategy in joint-market participation. Additionally, unlike conventional "black-box" DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS in the dynamic electricity market. We validate our method using realistic market prices from the Australian National Electricity Market. The results show that our strategy outperforms benchmarks, including both optimization-based and other DRL-based strategies, by substantial margins. Our findings further suggest that effective temporal-aware bidding can significantly increase profits in the spot and contingency FCAS markets compared to individual market participation.
|
[
"['Jinhao Li' 'Changlong Wang' 'Yanru Zhang' 'Hao Wang']"
] |
null | null |
2402.19142
| null | null |
http://arxiv.org/pdf/2402.19142v1
|
2024-02-29T13:25:15Z
|
2024-02-29T13:25:15Z
|
ProtoP-OD: Explainable Object Detection with Prototypical Parts
|
Interpretation and visualization of the behavior of detection transformers tends to highlight the locations in the image that the model attends to, but it provides limited insight into the emph{semantics} that the model is focusing on. This paper introduces an extension to detection transformers that constructs prototypical local features and uses them in object detection. These custom features, which we call prototypical parts, are designed to be mutually exclusive and align with the classifications of the model. The proposed extension consists of a bottleneck module, the prototype neck, that computes a discretized representation of prototype activations and a new loss term that matches prototypes to object classes. This setup leads to interpretable representations in the prototype neck, allowing visual inspection of the image content perceived by the model and a better understanding of the model's reliability. We show experimentally that our method incurs only a limited performance penalty, and we provide examples that demonstrate the quality of the explanations provided by our method, which we argue outweighs the performance penalty.
|
[
"['Pavlos Rath-Manakidis' 'Frederik Strothmann' 'Tobias Glasmachers'\n 'Laurenz Wiskott']"
] |
null | null |
2402.19155
| null | null |
http://arxiv.org/pdf/2402.19155v1
|
2024-02-29T13:38:07Z
|
2024-02-29T13:38:07Z
|
Beyond Language Models: Byte Models are Digital World Simulators
|
Traditional deep learning often overlooks bytes, the basic units of the digital world, where all forms of information and operations are encoded and manipulated in binary format. Inspired by the success of next token prediction in natural language processing, we introduce bGPT, a model with next byte prediction to simulate the digital world. bGPT matches specialized models in performance across various modalities, including text, audio, and images, and offers new possibilities for predicting, simulating, and diagnosing algorithm or hardware behaviour. It has almost flawlessly replicated the process of converting symbolic music data, achieving a low error rate of 0.0011 bits per byte in converting ABC notation to MIDI format. In addition, bGPT demonstrates exceptional capabilities in simulating CPU behaviour, with an accuracy exceeding 99.99% in executing various operations. Leveraging next byte prediction, models like bGPT can directly learn from vast binary data, effectively simulating the intricate patterns of the digital world.
|
[
"['Shangda Wu' 'Xu Tan' 'Zili Wang' 'Rui Wang' 'Xiaobing Li' 'Maosong Sun']"
] |
null | null |
2402.19163
| null | null |
http://arxiv.org/pdf/2402.19163v2
|
2024-06-20T08:18:17Z
|
2024-02-29T13:47:23Z
|
Decoupled Subgraph Federated Learning
|
We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subgraphs, where interconnections between different clients play a critical role. We present a novel framework for this scenario, named FedStruct, that harnesses deep structural dependencies. To uphold privacy, unlike existing methods, FedStruct eliminates the necessity of sharing or generating sensitive node features or embeddings among clients. Instead, it leverages explicit global graph structure information to capture inter-node dependencies. We validate the effectiveness of FedStruct through experimental results conducted on six datasets for semi-supervised node classification, showcasing performance close to the centralized approach across various scenarios, including different data partitioning methods, varying levels of label availability, and number of clients.
|
[
"['Javad Aliakbari' 'Johan Östman' 'Alexandre Graell i Amat']"
] |
null | null |
2402.19186
| null | null |
http://arxiv.org/pdf/2402.19186v1
|
2024-02-29T14:11:08Z
|
2024-02-29T14:11:08Z
|
Disentangling representations of retinal images with generative models
|
Retinal fundus images play a crucial role in the early detection of eye diseases and, using deep learning approaches, recent studies have even demonstrated their potential for detecting cardiovascular risk factors and neurological disorders. However, the impact of technical factors on these images can pose challenges for reliable AI applications in ophthalmology. For example, large fundus cohorts are often confounded by factors like camera type, image quality or illumination level, bearing the risk of learning shortcuts rather than the causal relationships behind the image generation process. Here, we introduce a novel population model for retinal fundus images that effectively disentangles patient attributes from camera effects, thus enabling controllable and highly realistic image generation. To achieve this, we propose a novel disentanglement loss based on distance correlation. Through qualitative and quantitative analyses, we demonstrate the effectiveness of this novel loss function in disentangling the learned subspaces. Our results show that our model provides a new perspective on the complex relationship between patient attributes and technical confounders in retinal fundus image generation.
|
[
"['Sarah Müller' 'Lisa M. Koch' 'Hendrik P. A. Lensch' 'Philipp Berens']"
] |
null | null |
2402.19197
| null | null |
http://arxiv.org/abs/2402.19197v1
|
2024-02-29T14:26:46Z
|
2024-02-29T14:26:46Z
|
Fine Structure-Aware Sampling: A New Sampling Training Scheme for
Pixel-Aligned Implicit Models in Single-View Human Reconstruction
|
Pixel-aligned implicit models, such as PIFu, PIFuHD, and ICON, are used for single-view clothed human reconstruction. These models need to be trained using a sampling training scheme. Existing sampling training schemes either fail to capture thin surfaces (e.g. ears, fingers) or cause noisy artefacts in reconstructed meshes. To address these problems, we introduce Fine Structured-Aware Sampling (FSS), a new sampling training scheme to train pixel-aligned implicit models for single-view human reconstruction. FSS resolves the aforementioned problems by proactively adapting to the thickness and complexity of surfaces. In addition, unlike existing sampling training schemes, FSS shows how normals of sample points can be capitalized in the training process to improve results. Lastly, to further improve the training process, FSS proposes a mesh thickness loss signal for pixel-aligned implicit models. It becomes computationally feasible to introduce this loss once a slight reworking of the pixel-aligned implicit function framework is carried out. Our results show that our methods significantly outperform SOTA methods qualitatively and quantitatively. Our code is publicly available at https://github.com/kcyt/FSS.
|
[
"['Kennard Yanting Chan' 'Fayao Liu' 'Guosheng Lin' 'Chuan Sheng Foo'\n 'Weisi Lin']"
] |
null | null |
2402.19212
| null | null |
http://arxiv.org/pdf/2402.19212v6
|
2024-06-24T16:23:42Z
|
2024-02-29T14:41:31Z
|
Deep Reinforcement Learning: A Convex Optimization Approach
|
In this paper, we consider reinforcement learning of nonlinear systems with continuous state and action spaces. We present an episodic learning algorithm, where we for each episode use convex optimization to find a two-layer neural network approximation of the optimal $Q$-function. The convex optimization approach guarantees that the weights calculated at each episode are optimal, with respect to the given sampled states and actions of the current episode. For stable nonlinear systems, we show that the algorithm converges and that the converging parameters of the trained neural network can be made arbitrarily close to the optimal neural network parameters. In particular, if the regularization parameter in the training phase is given by $rho$, then the parameters of the trained neural network converge to $w$, where the distance between $w$ and the optimal parameters $w^star$ is bounded by $mathcal{O}(rho)$. That is, when the number of episodes goes to infinity, there exists a constant $C$ such that [ |w-w^star| le Crho. ] In particular, our algorithm converges arbitrarily close to the optimal neural network parameters as the regularization parameter goes to zero. As a consequence, our algorithm converges fast due to the polynomial-time convergence of convex optimization algorithms.
|
[
"['Ather Gattami']"
] |
null | null |
2402.19226
| null | null |
http://arxiv.org/pdf/2402.19226v3
|
2024-06-14T17:32:32Z
|
2024-02-29T14:58:15Z
|
Investigating Gender Fairness in Machine Learning-driven Personalized
Care for Chronic Pain
|
Chronic pain significantly diminishes the quality of life for millions worldwide. While psychoeducation and therapy can improve pain outcomes, many individuals experiencing pain lack access to evidence-based treatments or fail to complete the necessary number of sessions to achieve benefit. Reinforcement learning (RL) shows potential in tailoring personalized pain management interventions according to patients' individual needs while ensuring the efficient use of scarce clinical resources. However, clinicians, patients, and healthcare decision-makers are concerned that RL solutions could exacerbate disparities associated with patient characteristics like race or gender. In this article, we study gender fairness in personalized pain care recommendations using a real-world application of reinforcement learning (Piette et al., 2022a). Here, adhering to gender fairness translates to minimal or no disparity in the utility received by subpopulations as defined by gender. We investigate whether the selection of relevant patient information (referred to as features) used to assist decision-making affects gender fairness. Our experiments, conducted using real-world data Piette, 2022), indicate that included features can impact gender fairness. Moreover, we propose an RL solution, NestedRecommendation, that demonstrates the ability: i) to adaptively learn to select the features that optimize for utility and fairness, and ii) to accelerate feature selection and in turn, improve pain care recommendations from early on, by leveraging clinicians' domain expertise.
|
[
"['Pratik Gajane' 'Sean Newman' 'Mykola Pechenizkiy' 'John D. Piette']"
] |
null | null |
2402.19232
| null | null |
http://arxiv.org/pdf/2402.19232v1
|
2024-02-29T15:05:59Z
|
2024-02-29T15:05:59Z
|
Trained Random Forests Completely Reveal your Dataset
|
We introduce an optimization-based reconstruction attack capable of completely or near-completely reconstructing a dataset utilized for training a random forest. Notably, our approach relies solely on information readily available in commonly used libraries such as scikit-learn. To achieve this, we formulate the reconstruction problem as a combinatorial problem under a maximum likelihood objective. We demonstrate that this problem is NP-hard, though solvable at scale using constraint programming -- an approach rooted in constraint propagation and solution-domain reduction. Through an extensive computational investigation, we demonstrate that random forests trained without bootstrap aggregation but with feature randomization are susceptible to a complete reconstruction. This holds true even with a small number of trees. Even with bootstrap aggregation, the majority of the data can also be reconstructed. These findings underscore a critical vulnerability inherent in widely adopted ensemble methods, warranting attention and mitigation. Although the potential for such reconstruction attacks has been discussed in privacy research, our study provides clear empirical evidence of their practicability.
|
[
"['Julien Ferry' 'Ricardo Fukasawa' 'Timothée Pascal' 'Thibaut Vidal']"
] |
null | null |
2402.19242
| null | null |
http://arxiv.org/pdf/2402.19242v1
|
2024-02-29T15:18:37Z
|
2024-02-29T15:18:37Z
|
Derivative-enhanced Deep Operator Network
|
Deep operator networks (DeepONets), a class of neural operators that learn mappings between function spaces, have recently been developed as surrogate models for parametric partial differential equations (PDEs). In this work we propose a derivative-enhanced deep operator network (DE-DeepONet), which leverages the derivative information to enhance the prediction accuracy, and provide a more accurate approximation of the derivatives, especially when the training data are limited. DE-DeepONet incorporates dimension reduction of input into DeepONet and includes two types of derivative labels in the loss function for training, that is, the directional derivatives of the output function with respect to the input function and the gradient of the output function with respect to the physical domain variables. We test DE-DeepONet on three different equations with increasing complexity to demonstrate its effectiveness compared to the vanilla DeepONet.
|
[
"['Yuan Qiu' 'Nolan Bridges' 'Peng Chen']"
] |
null | null |
2402.19254
| null | null |
http://arxiv.org/pdf/2402.19254v1
|
2024-02-29T15:26:03Z
|
2024-02-29T15:26:03Z
|
Machine learning for modular multiplication
|
Motivated by cryptographic applications, we investigate two machine learning approaches to modular multiplication: namely circular regression and a sequence-to-sequence transformer model. The limited success of both methods demonstrated in our results gives evidence for the hardness of tasks involving modular multiplication upon which cryptosystems are based.
|
[
"['Kristin Lauter' 'Cathy Yuanchen Li' 'Krystal Maughan' 'Rachel Newton'\n 'Megha Srivastava']"
] |
null | null |
2402.19262
| null | null |
http://arxiv.org/pdf/2402.19262v1
|
2024-02-29T15:32:02Z
|
2024-02-29T15:32:02Z
|
Masks, Signs, And Learning Rate Rewinding
|
Learning Rate Rewinding (LRR) has been established as a strong variant of Iterative Magnitude Pruning (IMP) to find lottery tickets in deep overparameterized neural networks. While both iterative pruning schemes couple structure and parameter learning, understanding how LRR excels in both aspects can bring us closer to the design of more flexible deep learning algorithms that can optimize diverse sets of sparse architectures. To this end, we conduct experiments that disentangle the effect of mask learning and parameter optimization and how both benefit from overparameterization. The ability of LRR to flip parameter signs early and stay robust to sign perturbations seems to make it not only more effective in mask identification but also in optimizing diverse sets of masks, including random ones. In support of this hypothesis, we prove in a simplified single hidden neuron setting that LRR succeeds in more cases than IMP, as it can escape initially problematic sign configurations.
|
[
"['Advait Gadhikar' 'Rebekka Burkholz']"
] |
null | null |
2402.19265
| null | null |
http://arxiv.org/abs/2402.19265v1
|
2024-02-29T15:36:01Z
|
2024-02-29T15:36:01Z
|
Learning Logic Specifications for Policy Guidance in POMDPs: an
Inductive Logic Programming Approach
|
Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perform online planning. However, scaling to complex realistic domains with many actions and long planning horizons is still a major challenge, and a key point to achieve good performance is guiding the action-selection process with domain-dependent policy heuristics which are tailored for the specific application domain. We propose to learn high-quality heuristics from POMDP traces of executions generated by any solver. We convert the belief-action pairs to a logical semantics, and exploit data- and time-efficient Inductive Logic Programming (ILP) to generate interpretable belief-based policy specifications, which are then used as online heuristics. We evaluate thoroughly our methodology on two notoriously challenging POMDP problems, involving large action spaces and long planning horizons, namely, rocksample and pocman. Considering different state-of-the-art online POMDP solvers, including POMCP, DESPOT and AdaOPS, we show that learned heuristics expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specific heuristics within lower computational time. Moreover, they well generalize to more challenging scenarios not experienced in the training phase (e.g., increasing rocks and grid size in rocksample, incrementing the size of the map and the aggressivity of ghosts in pocman).
|
[
"['Daniele Meli' 'Alberto Castellini' 'Alessandro Farinelli']"
] |
null | null |
2402.19275
| null | null |
http://arxiv.org/pdf/2402.19275v1
|
2024-02-29T15:42:33Z
|
2024-02-29T15:42:33Z
|
Adaptive Testing Environment Generation for Connected and Automated
Vehicles with Dense Reinforcement Learning
|
The assessment of safety performance plays a pivotal role in the development and deployment of connected and automated vehicles (CAVs). A common approach involves designing testing scenarios based on prior knowledge of CAVs (e.g., surrogate models), conducting tests in these scenarios, and subsequently evaluating CAVs' safety performances. However, substantial differences between CAVs and the prior knowledge can significantly diminish the evaluation efficiency. In response to this issue, existing studies predominantly concentrate on the adaptive design of testing scenarios during the CAV testing process. Yet, these methods have limitations in their applicability to high-dimensional scenarios. To overcome this challenge, we develop an adaptive testing environment that bolsters evaluation robustness by incorporating multiple surrogate models and optimizing the combination coefficients of these surrogate models to enhance evaluation efficiency. We formulate the optimization problem as a regression task utilizing quadratic programming. To efficiently obtain the regression target via reinforcement learning, we propose the dense reinforcement learning method and devise a new adaptive policy with high sample efficiency. Essentially, our approach centers on learning the values of critical scenes displaying substantial surrogate-to-real gaps. The effectiveness of our method is validated in high-dimensional overtaking scenarios, demonstrating that our approach achieves notable evaluation efficiency.
|
[
"['Jingxuan Yang' 'Ruoxuan Bai' 'Haoyuan Ji' 'Yi Zhang' 'Jianming Hu'\n 'Shuo Feng']"
] |
null | null |
2402.19287
| null | null |
http://arxiv.org/pdf/2402.19287v1
|
2024-02-29T15:52:21Z
|
2024-02-29T15:52:21Z
|
StiefelGen: A Simple, Model Agnostic Approach for Time Series Data
Augmentation over Riemannian Manifolds
|
Data augmentation is an area of research which has seen active development in many machine learning fields, such as in image-based learning models, reinforcement learning for self driving vehicles, and general noise injection for point cloud data. However, convincing methods for general time series data augmentation still leaves much to be desired, especially since the methods developed for these models do not readily cross-over. Three common approaches for time series data augmentation include: (i) Constructing a physics-based model and then imbuing uncertainty over the coefficient space (for example), (ii) Adding noise to the observed data set(s), and, (iii) Having access to ample amounts of time series data sets from which a robust generative neural network model can be trained. However, for many practical problems that work with time series data in the industry: (i) One usually does not have access to a robust physical model, (ii) The addition of noise can in of itself require large or difficult assumptions (for example, what probability distribution should be used? Or, how large should the noise variance be?), and, (iii) In practice, it can be difficult to source a large representative time series data base with which to train the neural network model for the underlying problem. In this paper, we propose a methodology which attempts to simultaneously tackle all three of these previous limitations to a large extent. The method relies upon the well-studied matrix differential geometry of the Stiefel manifold, as it proposes a simple way in which time series signals can placed on, and then smoothly perturbed over the manifold. We attempt to clarify how this method works by showcasing several potential use cases which in particular work to take advantage of the unique properties of this underlying manifold.
|
[
"['Prasad Cheema' 'Mahito Sugiyama']"
] |
null | null |
2402.19290
| null | null |
http://arxiv.org/pdf/2402.19290v2
|
2024-03-06T14:14:24Z
|
2024-02-29T15:53:47Z
|
Estimation and Deconvolution of Second Order Cyclostationary Signals
|
This method solves the dual problem of blind deconvolution and estimation of the time waveform of noisy second-order cyclo-stationary (CS2) signals that traverse a Transfer Function (TF) en route to a sensor. We have proven that the deconvolution filter exists and eliminates the TF effect from signals whose statistics vary over time. This method is blind, meaning it does not require prior knowledge about the signals or TF. Simulations demonstrate the algorithm high precision across various signal types, TFs, and Signal-to-Noise Ratios (SNRs). In this study, the CS2 signals family is restricted to the product of a deterministic periodic function and white noise. Furthermore, this method has the potential to improve the training of Machine Learning models where the aggregation of signals from identical systems but with different TFs is required.
|
[
"['Igor Makienko' 'Michael Grebshtein' 'Eli Gildish']"
] |
null | null |
2402.19294
| null | null |
http://arxiv.org/pdf/2402.19294v1
|
2024-02-29T15:57:09Z
|
2024-02-29T15:57:09Z
|
Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes
|
Operating units often experience various failure modes in complex systems, leading to distinct degradation paths. Relying on a prognostic model trained on a single failure mode may lead to poor generalization performance across multiple failure modes. Therefore, accurately identifying the failure mode is of critical importance. Current prognostic approaches either ignore failure modes during degradation or assume known failure mode labels, which can be challenging to acquire in practice. Moreover, the high dimensionality and complex relations of sensor signals make it challenging to identify the failure modes accurately. To address these issues, we propose a novel failure mode diagnosis method that leverages a dimension reduction technique called UMAP (Uniform Manifold Approximation and Projection) to project and visualize each unit's degradation trajectory into a lower dimension. Then, using these degradation trajectories, we develop a time series-based clustering method to identify the training units' failure modes. Finally, we introduce a monotonically constrained prognostic model to predict the failure mode labels and RUL of the test units simultaneously using the obtained failure modes of the training units. The proposed prognostic model provides failure mode-specific RUL predictions while preserving the monotonic property of the RUL predictions across consecutive time steps. We evaluate the proposed model using a case study with the aircraft gas turbine engine dataset.
|
[
"['Ying Fu' 'Ye Kwon Huh' 'Kaibo Liu']"
] |
null | null |
2402.19295
| null | null |
http://arxiv.org/pdf/2402.19295v1
|
2024-02-29T15:58:16Z
|
2024-02-29T15:58:16Z
|
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical
Bayesian Modelling
|
Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structures. However, benign variations exist among members, such as geometry, sea-bed conditions and temperature differences. These factors could influence structural properties and therefore the dynamic response, making it more difficult to detect structural problems via traditional SHM techniques. This paper explores the use of a hierarchical Bayesian model to infer expected soil stiffness distributions at both population and local levels, as a basis to perform anomaly detection, in the form of scour, for new and existing turbines. To do this, observations of natural frequency will be generated as though they are from a small population of wind turbines. Differences between individual observations will be introduced by postulating distributions over the soil stiffness and measurement noise, as well as reducing soil depth (to represent scour), in the case of anomaly detection.
|
[
"['S. M. Smith' 'A. J. Hughes' 'T. A. Dardeno' 'L. A. Bull' 'N. Dervilis'\n 'K. Worden']"
] |
null | null |
2402.19296
| null | null |
http://arxiv.org/pdf/2402.19296v1
|
2024-02-29T15:59:42Z
|
2024-02-29T15:59:42Z
|
An AI based Digital Score of Tumour-Immune Microenvironment Predicts
Benefit to Maintenance Immunotherapy in Advanced Oesophagogastric
Adenocarcinoma
|
Gastric and oesophageal (OG) cancers are the leading causes of cancer mortality worldwide. In OG cancers, recent studies have showed that PDL1 immune checkpoint inhibitors (ICI) in combination with chemotherapy improves patient survival. However, our understanding of the tumour immune microenvironment in OG cancers remains limited. In this study, we interrogate multiplex immunofluorescence (mIF) images taken from patients with advanced Oesophagogastric Adenocarcinoma (OGA) who received first-line fluoropyrimidine and platinum-based chemotherapy in the PLATFORM trial (NCT02678182) to predict the efficacy of the treatment and to explore the biological basis of patients responding to maintenance durvalumab (PDL1 inhibitor). Our proposed Artificial Intelligence (AI) based marker successfully identified responder from non-responder (p < 0.05) as well as those who could potentially benefit from ICI with statistical significance (p < 0.05) for both progression free and overall survival. Our findings suggest that T cells that express FOXP3 seem to heavily influence the patient treatment response and survival outcome. We also observed that higher levels of CD8+PD1+ cells are consistently linked to poor prognosis for both OS and PFS, regardless of ICI.
|
[
"['Quoc Dang Vu' 'Caroline Fong' 'Anderley Gordon' 'Tom Lund'\n 'Tatiany L Silveira' 'Daniel Rodrigues' 'Katharina von Loga'\n 'Shan E Ahmed Raza' 'David Cunningham' 'Nasir Rajpoot']"
] |
null | null |
2402.19299
| null | null |
http://arxiv.org/pdf/2402.19299v1
|
2024-02-29T16:07:22Z
|
2024-02-29T16:07:22Z
|
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
|
Large Language Models (LLMs) have demonstrated proficiency in utilizing various tools by coding, yet they face limitations in handling intricate logic and precise control. In embodied tasks, high-level planning is amenable to direct coding, while low-level actions often necessitate task-specific refinement, such as Reinforcement Learning (RL). To seamlessly integrate both modalities, we introduce a two-level hierarchical framework, RL-GPT, comprising a slow agent and a fast agent. The slow agent analyzes actions suitable for coding, while the fast agent executes coding tasks. This decomposition effectively focuses each agent on specific tasks, proving highly efficient within our pipeline. Our approach outperforms traditional RL methods and existing GPT agents, demonstrating superior efficiency. In the Minecraft game, it rapidly obtains diamonds within a single day on an RTX3090. Additionally, it achieves SOTA performance across all designated MineDojo tasks.
|
[
"['Shaoteng Liu' 'Haoqi Yuan' 'Minda Hu' 'Yanwei Li' 'Yukang Chen'\n 'Shu Liu' 'Zongqing Lu' 'Jiaya Jia']"
] |
null | null |
2402.19303
| null | null |
http://arxiv.org/pdf/2402.19303v1
|
2024-02-29T16:09:19Z
|
2024-02-29T16:09:19Z
|
Learnability Gaps of Strategic Classification
|
In contrast with standard classification tasks, strategic classification involves agents strategically modifying their features in an effort to receive favorable predictions. For instance, given a classifier determining loan approval based on credit scores, applicants may open or close their credit cards to fool the classifier. The learning goal is to find a classifier robust against strategic manipulations. Various settings, based on what and when information is known, have been explored in strategic classification. In this work, we focus on addressing a fundamental question: the learnability gaps between strategic classification and standard learning. We essentially show that any learnable class is also strategically learnable: we first consider a fully informative setting, where the manipulation structure (which is modeled by a manipulation graph $G^star$) is known and during training time the learner has access to both the pre-manipulation data and post-manipulation data. We provide nearly tight sample complexity and regret bounds, offering significant improvements over prior results. Then, we relax the fully informative setting by introducing two natural types of uncertainty. First, following Ahmadi et al. (2023), we consider the setting in which the learner only has access to the post-manipulation data. We improve the results of Ahmadi et al. (2023) and close the gap between mistake upper bound and lower bound raised by them. Our second relaxation of the fully informative setting introduces uncertainty to the manipulation structure. That is, we assume that the manipulation graph is unknown but belongs to a known class of graphs. We provide nearly tight bounds on the learning complexity in various unknown manipulation graph settings. Notably, our algorithm in this setting is of independent interest and can be applied to other problems such as multi-label learning.
|
[
"['Lee Cohen' 'Yishay Mansour' 'Shay Moran' 'Han Shao']"
] |
null | null |
2402.19308
| null | null |
http://arxiv.org/pdf/2402.19308v1
|
2024-02-29T16:15:34Z
|
2024-02-29T16:15:34Z
|
Loss-Free Machine Unlearning
|
We present a machine unlearning approach that is both retraining- and label-free. Most existing machine unlearning approaches require a model to be fine-tuned to remove information while preserving performance. This is computationally expensive and necessitates the storage of the whole dataset for the lifetime of the model. Retraining-free approaches often utilise Fisher information, which is derived from the loss and requires labelled data which may not be available. Thus, we present an extension to the Selective Synaptic Dampening algorithm, substituting the diagonal of the Fisher information matrix for the gradient of the l2 norm of the model output to approximate sensitivity. We evaluate our method in a range of experiments using ResNet18 and Vision Transformer. Results show our label-free method is competitive with existing state-of-the-art approaches.
|
[
"['Jack Foster' 'Stefan Schoepf' 'Alexandra Brintrup']"
] |
null | null |
2402.19319
| null | null |
http://arxiv.org/pdf/2402.19319v1
|
2024-02-29T16:24:19Z
|
2024-02-29T16:24:19Z
|
Attacks Against Mobility Prediction in 5G Networks
|
The $5^{th}$ generation of mobile networks introduces a new Network Function (NF) that was not present in previous generations, namely the Network Data Analytics Function (NWDAF). Its primary objective is to provide advanced analytics services to various entities within the network and also towards external application services in the 5G ecosystem. One of the key use cases of NWDAF is mobility trajectory prediction, which aims to accurately support efficient mobility management of User Equipment (UE) in the network by allocating ``just in time'' necessary network resources. In this paper, we show that there are potential mobility attacks that can compromise the accuracy of these predictions. In a semi-realistic scenario with 10,000 subscribers, we demonstrate that an adversary equipped with the ability to hijack cellular mobile devices and clone them can significantly reduce the prediction accuracy from 75% to 40% using just 100 adversarial UEs. While a defense mechanism largely depends on the attack and the mobility types in a particular area, we prove that a basic KMeans clustering is effective in distinguishing legitimate and adversarial UEs.
|
[
"['Syafiq Al Atiiq' 'Yachao Yuan' 'Christian Gehrmann' 'Jakob Sternby'\n 'Luis Barriga']"
] |
null | null |
2402.19322
| null | null |
http://arxiv.org/pdf/2402.19322v2
|
2024-03-06T14:12:56Z
|
2024-02-29T16:27:59Z
|
Verification of Neural Networks' Global Robustness
|
Neural networks are successful in various applications but are also susceptible to adversarial attacks. To show the safety of network classifiers, many verifiers have been introduced to reason about the local robustness of a given input to a given perturbation. While successful, local robustness cannot generalize to unseen inputs. Several works analyze global robustness properties, however, neither can provide a precise guarantee about the cases where a network classifier does not change its classification. In this work, we propose a new global robustness property for classifiers aiming at finding the minimal globally robust bound, which naturally extends the popular local robustness property for classifiers. We introduce VHAGaR, an anytime verifier for computing this bound. VHAGaR relies on three main ideas: encoding the problem as a mixed-integer programming and pruning the search space by identifying dependencies stemming from the perturbation or the network's computation and generalizing adversarial attacks to unknown inputs. We evaluate VHAGaR on several datasets and classifiers and show that, given a three hour timeout, the average gap between the lower and upper bound on the minimal globally robust bound computed by VHAGaR is 1.9, while the gap of an existing global robustness verifier is 154.7. Moreover, VHAGaR is 130.6x faster than this verifier. Our results further indicate that leveraging dependencies and adversarial attacks makes VHAGaR 78.6x faster.
|
[
"['Anan Kabaha' 'Dana Drachsler-Cohen']"
] |
null | null |
2402.19348
| null | null |
http://arxiv.org/pdf/2402.19348v2
|
2024-06-16T10:16:00Z
|
2024-02-29T16:56:23Z
|
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy,
Advances, and Outlook
|
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data) and modalities (e.g., spatio-temporal, visual, and textual modalities). Recently, we are witnessing a rising trend that utilizes various deep-learning methods to facilitate cross-domain data fusion in smart cities. To this end, we propose the first survey that systematically reviews the latest advancements in deep learning-based data fusion methods tailored for urban computing. Specifically, we first delve into data perspective to comprehend the role of each modality and data source. Secondly, we classify the methodology into four primary categories: feature-based, alignment-based, contrast-based, and generation-based fusion methods. Thirdly, we further categorize multi-modal urban applications into seven types: urban planning, transportation, economy, public safety, society, environment, and energy. Compared with previous surveys, we focus more on the synergy of deep learning methods with urban computing applications. Furthermore, we shed light on the interplay between Large Language Models (LLMs) and urban computing, postulating future research directions that could revolutionize the field. We firmly believe that the taxonomy, progress, and prospects delineated in our survey stand poised to significantly enrich the research community. The summary of the comprehensive and up-to-date paper list can be found at https://github.com/yoshall/Awesome-Multimodal-Urban-Computing.
|
[
"['Xingchen Zou' 'Yibo Yan' 'Xixuan Hao' 'Yuehong Hu' 'Haomin Wen'\n 'Erdong Liu' 'Junbo Zhang' 'Yong Li' 'Tianrui Li' 'Yu Zheng'\n 'Yuxuan Liang']"
] |
null | null |
2402.19355
| null | null |
http://arxiv.org/pdf/2402.19355v1
|
2024-02-29T17:06:52Z
|
2024-02-29T17:06:52Z
|
Unraveling Adversarial Examples against Speaker Identification --
Techniques for Attack Detection and Victim Model Classification
|
Adversarial examples have proven to threaten speaker identification systems, and several countermeasures against them have been proposed. In this paper, we propose a method to detect the presence of adversarial examples, i.e., a binary classifier distinguishing between benign and adversarial examples. We build upon and extend previous work on attack type classification by exploring new architectures. Additionally, we introduce a method for identifying the victim model on which the adversarial attack is carried out. To achieve this, we generate a new dataset containing multiple attacks performed against various victim models. We achieve an AUC of 0.982 for attack detection, with no more than a 0.03 drop in performance for unknown attacks. Our attack classification accuracy (excluding benign) reaches 86.48% across eight attack types using our LightResNet34 architecture, while our victim model classification accuracy reaches 72.28% across four victim models.
|
[
"['Sonal Joshi' 'Thomas Thebaud' 'Jesús Villalba' 'Najim Dehak']"
] |
null | null |
2402.19361
| null | null |
http://arxiv.org/pdf/2402.19361v2
|
2024-06-24T14:48:29Z
|
2024-02-29T17:12:39Z
|
Watermark Stealing in Large Language Models
|
LLM watermarking has attracted attention as a promising way to detect AI-generated content, with some works suggesting that current schemes may already be fit for deployment. In this work we dispute this claim, identifying watermark stealing (WS) as a fundamental vulnerability of these schemes. We show that querying the API of the watermarked LLM to approximately reverse-engineer a watermark enables practical spoofing attacks, as hypothesized in prior work, but also greatly boosts scrubbing attacks, which was previously unnoticed. We are the first to propose an automated WS algorithm and use it in the first comprehensive study of spoofing and scrubbing in realistic settings. We show that for under $50 an attacker can both spoof and scrub state-of-the-art schemes previously considered safe, with average success rate of over 80%. Our findings challenge common beliefs about LLM watermarking, stressing the need for more robust schemes. We make all our code and additional examples available at https://watermark-stealing.org.
|
[
"['Nikola Jovanović' 'Robin Staab' 'Martin Vechev']"
] |
null | null |
2402.19369
| null | null |
http://arxiv.org/pdf/2402.19369v1
|
2024-02-29T17:16:20Z
|
2024-02-29T17:16:20Z
|
Structure Preserving Diffusion Models
|
Diffusion models have become the leading distribution-learning method in recent years. Herein, we introduce structure-preserving diffusion processes, a family of diffusion processes for learning distributions that possess additional structure, such as group symmetries, by developing theoretical conditions under which the diffusion transition steps preserve said symmetry. While also enabling equivariant data sampling trajectories, we exemplify these results by developing a collection of different symmetry equivariant diffusion models capable of learning distributions that are inherently symmetric. Empirical studies, over both synthetic and real-world datasets, are used to validate the developed models adhere to the proposed theory and are capable of achieving improved performance over existing methods in terms of sample equality. We also show how the proposed models can be used to achieve theoretically guaranteed equivariant image noise reduction without prior knowledge of the image orientation.
|
[
"['Haoye Lu' 'Spencer Szabados' 'Yaoliang Yu']"
] |
null | null |
2402.19379
| null | null |
http://arxiv.org/pdf/2402.19379v5
|
2024-06-17T11:38:00Z
|
2024-02-29T17:27:59Z
|
Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival
Human Crowd Accuracy
|
Human forecasting accuracy in practice relies on the 'wisdom of the crowd' effect, in which predictions about future events are significantly improved by aggregating across a crowd of individual forecasters. Past work on the forecasting ability of large language models (LLMs) suggests that frontier LLMs, as individual forecasters, underperform compared to the gold standard of a human-crowd forecasting-tournament aggregate. In Study 1, we expand this research by using an LLM ensemble approach consisting of a crowd of 12 LLMs. We compare the aggregated LLM predictions on 31 binary questions to those of a crowd of 925 human forecasters from a three-month forecasting tournament. Our preregistered main analysis shows that the LLM crowd outperforms a simple no-information benchmark, and is not statistically different from the human crowd. We also observe a set of human-like biases in machine responses, such as an acquiescence effect and a tendency to favour round numbers. In Study 2, we test whether LLM predictions (of GPT-4 and Claude 2) can be improved by drawing on human cognitive output. We find that both models' forecasting accuracy benefits from exposure to the median human prediction as information, improving accuracy by between 17% and 28%, though this leads to less accurate predictions than simply averaging human and machine forecasts. Our results suggest that LLMs can achieve forecasting accuracy rivaling that of the human crowd: via the simple, practically applicable method of forecast aggregation.
|
[
"['Philipp Schoenegger' 'Indre Tuminauskaite' 'Peter S. Park'\n 'Rafael Valdece Sousa Bastos' 'Philip E. Tetlock']"
] |
null | null |
2402.19402
| null | null |
http://arxiv.org/abs/2402.19402v1
|
2024-02-29T18:01:07Z
|
2024-02-29T18:01:07Z
|
A Scalable and Transferable Time Series Prediction Framework for Demand
Forecasting
|
Time series forecasting is one of the most essential and ubiquitous tasks in many business problems, including demand forecasting and logistics optimization. Traditional time series forecasting methods, however, have resulted in small models with limited expressive power because they have difficulty in scaling their model size up while maintaining high accuracy. In this paper, we propose Forecasting orchestra (Forchestra), a simple but powerful framework capable of accurately predicting future demand for a diverse range of items. We empirically demonstrate that the model size is scalable to up to 0.8 billion parameters. The proposed method not only outperforms existing forecasting models with a significant margin, but it could generalize well to unseen data points when evaluated in a zero-shot fashion on downstream datasets. Last but not least, we present extensive qualitative and quantitative studies to analyze how the proposed model outperforms baseline models and differs from conventional approaches. The original paper was presented as a full paper at ICDM 2022 and is available at: https://ieeexplore.ieee.org/document/10027662.
|
[
"['Young-Jin Park' 'Donghyun Kim' 'Frédéric Odermatt' 'Juho Lee'\n 'Kyung-Min Kim']"
] |
null | null |
2402.19411
| null | null |
http://arxiv.org/pdf/2402.19411v1
|
2024-02-29T18:09:03Z
|
2024-02-29T18:09:03Z
|
PaECTER: Patent-level Representation Learning using Citation-informed
Transformers
|
PaECTER is a publicly available, open-source document-level encoder specific for patents. We fine-tune BERT for Patents with examiner-added citation information to generate numerical representations for patent documents. PaECTER performs better in similarity tasks than current state-of-the-art models used in the patent domain. More specifically, our model outperforms the next-best patent specific pre-trained language model (BERT for Patents) on our patent citation prediction test dataset on two different rank evaluation metrics. PaECTER predicts at least one most similar patent at a rank of 1.32 on average when compared against 25 irrelevant patents. Numerical representations generated by PaECTER from patent text can be used for downstream tasks such as classification, tracing knowledge flows, or semantic similarity search. Semantic similarity search is especially relevant in the context of prior art search for both inventors and patent examiners. PaECTER is available on Hugging Face.
|
[
"['Mainak Ghosh' 'Sebastian Erhardt' 'Michael E. Rose' 'Erik Buunk'\n 'Dietmar Harhoff']"
] |
null | null |
2402.19427
| null | null |
http://arxiv.org/pdf/2402.19427v1
|
2024-02-29T18:24:46Z
|
2024-02-29T18:24:46Z
|
Griffin: Mixing Gated Linear Recurrences with Local Attention for
Efficient Language Models
|
Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated linear recurrences, and Griffin, a hybrid model that mixes gated linear recurrences with local attention. Hawk exceeds the reported performance of Mamba on downstream tasks, while Griffin matches the performance of Llama-2 despite being trained on over 6 times fewer tokens. We also show that Griffin can extrapolate on sequences significantly longer than those seen during training. Our models match the hardware efficiency of Transformers during training, and during inference they have lower latency and significantly higher throughput. We scale Griffin up to 14B parameters, and explain how to shard our models for efficient distributed training.
|
[
"['Soham De' 'Samuel L. Smith' 'Anushan Fernando' 'Aleksandar Botev'\n 'George Cristian-Muraru' 'Albert Gu' 'Ruba Haroun' 'Leonard Berrada'\n 'Yutian Chen' 'Srivatsan Srinivasan' 'Guillaume Desjardins'\n 'Arnaud Doucet' 'David Budden' 'Yee Whye Teh' 'Razvan Pascanu'\n 'Nando De Freitas' 'Caglar Gulcehre']"
] |
null | null |
2402.19437
| null | null |
http://arxiv.org/pdf/2402.19437v1
|
2024-02-29T18:38:20Z
|
2024-02-29T18:38:20Z
|
Differentially Private Worst-group Risk Minimization
|
We initiate a systematic study of worst-group risk minimization under $(epsilon, delta)$-differential privacy (DP). The goal is to privately find a model that approximately minimizes the maximal risk across $p$ sub-populations (groups) with different distributions, where each group distribution is accessed via a sample oracle. We first present a new algorithm that achieves excess worst-group population risk of $tilde{O}(frac{psqrt{d}}{Kepsilon} + sqrt{frac{p}{K}})$, where $K$ is the total number of samples drawn from all groups and $d$ is the problem dimension. Our rate is nearly optimal when each distribution is observed via a fixed-size dataset of size $K/p$. Our result is based on a new stability-based analysis for the generalization error. In particular, we show that $Delta$-uniform argument stability implies $tilde{O}(Delta + frac{1}{sqrt{n}})$ generalization error w.r.t. the worst-group risk, where $n$ is the number of samples drawn from each sample oracle. Next, we propose an algorithmic framework for worst-group population risk minimization using any DP online convex optimization algorithm as a subroutine. Hence, we give another excess risk bound of $tilde{O}left( sqrt{frac{d^{1/2}}{epsilon K}} +sqrt{frac{p}{Kepsilon^2}} right)$. Assuming the typical setting of $epsilon=Theta(1)$, this bound is more favorable than our first bound in a certain range of $p$ as a function of $K$ and $d$. Finally, we study differentially private worst-group empirical risk minimization in the offline setting, where each group distribution is observed by a fixed-size dataset. We present a new algorithm with nearly optimal excess risk of $tilde{O}(frac{psqrt{d}}{Kepsilon})$.
|
[
"['Xinyu Zhou' 'Raef Bassily']"
] |
null | null |
2402.19442
| null | null |
http://arxiv.org/pdf/2402.19442v2
|
2024-06-10T17:18:07Z
|
2024-02-29T18:43:52Z
|
Training Dynamics of Multi-Head Softmax Attention for In-Context
Learning: Emergence, Convergence, and Optimality
|
We study the dynamics of gradient flow for training a multi-head softmax attention model for in-context learning of multi-task linear regression. We establish the global convergence of gradient flow under suitable choices of initialization. In addition, we prove that an interesting "task allocation" phenomenon emerges during the gradient flow dynamics, where each attention head focuses on solving a single task of the multi-task model. Specifically, we prove that the gradient flow dynamics can be split into three phases -- a warm-up phase where the loss decreases rather slowly and the attention heads gradually build up their inclination towards individual tasks, an emergence phase where each head selects a single task and the loss rapidly decreases, and a convergence phase where the attention parameters converge to a limit. Furthermore, we prove the optimality of gradient flow in the sense that the limiting model learned by gradient flow is on par with the best possible multi-head softmax attention model up to a constant factor. Our analysis also delineates a strict separation in terms of the prediction accuracy of ICL between single-head and multi-head attention models. The key technique for our convergence analysis is to map the gradient flow dynamics in the parameter space to a set of ordinary differential equations in the spectral domain, where the relative magnitudes of the semi-singular values of the attention weights determines task allocation. To our best knowledge, our work provides the first convergence result for the multi-head softmax attention model.
|
[
"['Siyu Chen' 'Heejune Sheen' 'Tianhao Wang' 'Zhuoran Yang']"
] |
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