categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.12957 | null | null | http://arxiv.org/pdf/2405.12957v2 | 2024-05-27T18:42:45Z | 2024-05-17T07:46:05Z | Enhancing the analysis of murine neonatal ultrasonic vocalizations:
Development, evaluation, and application of different mathematical models | Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactions, and developmental stages of animals, various deep learning approaches have aimed to automate both the quantitative (detection) and qualitative (classification) analysis of USVs. Here, we present the first systematic evaluation of different types of neural networks for USV classification. We assessed various feedforward networks, including a custom-built, fully-connected network and convolutional neural network, different residual neural networks (ResNets), an EfficientNet, and a Vision Transformer (ViT). Paired with a refined, entropy-based detection algorithm (achieving recall of 94.9% and precision of 99.3%), the best architecture (achieving 86.79% accuracy) was integrated into a fully automated pipeline capable of analyzing extensive USV datasets with high reliability. Additionally, users can specify an individual minimum accuracy threshold based on their research needs. In this semi-automated setup, the pipeline selectively classifies calls with high pseudo-probability, leaving the rest for manual inspection. Our study focuses exclusively on neonatal USVs. As part of an ongoing phenotyping study, our pipeline has proven to be a valuable tool for identifying key differences in USVs produced by mice with autism-like behaviors. | [
"['Rudolf Herdt' 'Louisa Kinzel' 'Johann Georg Maaß' 'Marvin Walther'\n 'Henning Fröhlich' 'Tim Schubert' 'Peter Maass'\n 'Christian Patrick Schaaf']"
] |
null | null | 2405.12958 | null | null | http://arxiv.org/pdf/2405.12958v1 | 2024-05-21T17:31:10Z | 2024-05-21T17:31:10Z | Online Learning of Halfspaces with Massart Noise | We study the task of online learning in the presence of Massart noise. Instead of assuming that the online adversary chooses an arbitrary sequence of labels, we assume that the context $mathbf{x}$ is selected adversarially but the label $y$ presented to the learner disagrees with the ground-truth label of $mathbf{x}$ with unknown probability at most $eta$. We study the fundamental class of $gamma$-margin linear classifiers and present a computationally efficient algorithm that achieves mistake bound $eta T + o(T)$. Our mistake bound is qualitatively tight for efficient algorithms: it is known that even in the offline setting achieving classification error better than $eta$ requires super-polynomial time in the SQ model. We extend our online learning model to a $k$-arm contextual bandit setting where the rewards -- instead of satisfying commonly used realizability assumptions -- are consistent (in expectation) with some linear ranking function with weight vector $mathbf{w}^ast$. Given a list of contexts $mathbf{x}_1,ldots mathbf{x}_k$, if $mathbf{w}^*cdot mathbf{x}_i > mathbf{w}^* cdot mathbf{x}_j$, the expected reward of action $i$ must be larger than that of $j$ by at least $Delta$. We use our Massart online learner to design an efficient bandit algorithm that obtains expected reward at least $(1-1/k)~ Delta T - o(T)$ bigger than choosing a random action at every round. | [
"['Ilias Diakonikolas' 'Vasilis Kontonis' 'Christos Tzamos' 'Nikos Zarifis']"
] |
null | null | 2405.12961 | null | null | http://arxiv.org/pdf/2405.12961v1 | 2024-05-21T17:35:20Z | 2024-05-21T17:35:20Z | Energy Rank Alignment: Using Preference Optimization to Search Chemical
Space at Scale | Searching through chemical space is an exceptionally challenging problem because the number of possible molecules grows combinatorially with the number of atoms. Large, autoregressive models trained on databases of chemical compounds have yielded powerful generators, but we still lack robust strategies for generating molecules with desired properties. This molecular search problem closely resembles the "alignment" problem for large language models, though for many chemical tasks we have a specific and easily evaluable reward function. Here, we introduce an algorithm called energy rank alignment (ERA) that leverages an explicit reward function to produce a gradient-based objective that we use to optimize autoregressive policies. We show theoretically that this algorithm is closely related to proximal policy optimization (PPO) and direct preference optimization (DPO), but has a minimizer that converges to an ideal Gibbs-Boltzmann distribution with the reward playing the role of an energy function. Furthermore, this algorithm is highly scalable, does not require reinforcement learning, and performs well relative to DPO when the number of preference observations per pairing is small. We deploy this approach to align molecular transformers to generate molecules with externally specified properties and find that it does so robustly, searching through diverse parts of chemical space. While our focus here is on chemical search, we also obtain excellent results on an AI supervised task for LLM alignment, showing that the method is scalable and general. | [
"['Shriram Chennakesavalu' 'Frank Hu' 'Sebastian Ibarraran'\n 'Grant M. Rotskoff']"
] |
null | null | 2405.12963 | null | null | http://arxiv.org/pdf/2405.12963v1 | 2024-05-21T17:44:48Z | 2024-05-21T17:44:48Z | Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a
Transformer Architecture: A Multicenter Study in Glioblastoma | Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability. Method: We propose and evaluate a transformer-based non-linear and non-proportional survival prediction model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with non-imaging data using cross-attention. To demonstrate model generalizability, the model is assessed with the time-dependent concordance index (Cdt) in two training setups using three independent public test sets: UPenn-GBM, UCSF-PDGM, and RHUH-GBM, each comprising 378, 366, and 36 cases, respectively. Results: The proposed transformer model achieved promising performance for imaging as well as non-imaging data, effectively integrating both modalities for enhanced performance (UPenn-GBM test-set, imaging Cdt 0.645, multimodal Cdt 0.707) while outperforming state-of-the-art late-fusion 3D-CNN-based models. Consistent performance was observed across the three independent multicenter test sets with Cdt values of 0.707 (UPenn-GBM, internal test set), 0.672 (UCSF-PDGM, first external test set) and 0.618 (RHUH-GBM, second external test set). The model achieved significant discrimination between patients with favorable and unfavorable survival for all three datasets (logrank p 1.9times{10}^{-8}, 9.7times{10}^{-3}, and 1.2times{10}^{-2}). Conclusions: The proposed transformer-based survival prediction model integrates complementary information from diverse input modalities, contributing to improved glioblastoma survival prediction compared to state-of-the-art methods. Consistent performance was observed across institutions supporting model generalizability. | [
"['Ahmed Gomaa' 'Yixing Huang' 'Amr Hagag' 'Charlotte Schmitter'\n 'Daniel Höfler' 'Thomas Weissmann' 'Katharina Breininger'\n 'Manuel Schmidt' 'Jenny Stritzelberger' 'Daniel Delev' 'Roland Coras'\n 'Arnd Dörfler' 'Oliver Schnell' 'Benjamin Frey' 'Udo S. Gaipl'\n 'Sabine Semrau' 'Christoph Bert' 'Rainer Fietkau' 'Florian Putz']"
] |
null | null | 2405.12965 | null | null | http://arxiv.org/pdf/2405.12965v1 | 2024-05-21T17:45:36Z | 2024-05-21T17:45:36Z | The future of cosmological likelihood-based inference: accelerated
high-dimensional parameter estimation and model comparison | We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings. Specifically, we combine (i) emulation, where a machine learning model is trained to mimic cosmological observables, e.g. CosmoPower-JAX; (ii) differentiable and probabilistic programming, e.g. JAX and NumPyro, respectively; (iii) scalable Markov chain Monte Carlo (MCMC) sampling techniques that exploit gradients, e.g. Hamiltonian Monte Carlo; and (iv) decoupled and scalable Bayesian model selection techniques that compute the Bayesian evidence purely from posterior samples, e.g. the learned harmonic mean implemented in harmonic. This paradigm allows us to carry out a complete Bayesian analysis, including both parameter estimation and model selection, in a fraction of the time of traditional approaches. First, we demonstrate the application of this paradigm on a simulated cosmic shear analysis for a Stage IV survey in 37- and 39-dimensional parameter spaces, comparing $Lambda$CDM and a dynamical dark energy model ($w_0w_a$CDM). We recover posterior contours and evidence estimates that are in excellent agreement with those computed by the traditional nested sampling approach while reducing the computational cost from 8 months on 48 CPU cores to 2 days on 12 GPUs. Second, we consider a joint analysis between three simulated next-generation surveys, each performing a 3x2pt analysis, resulting in 157- and 159-dimensional parameter spaces. Standard nested sampling techniques are simply not feasible in this high-dimensional setting, requiring a projected 12 years of compute time on 48 CPU cores; on the other hand, the proposed approach only requires 8 days of compute time on 24 GPUs. All packages used in our analyses are publicly available. | [
"['Davide Piras' 'Alicja Polanska' 'Alessio Spurio Mancini'\n 'Matthew A. Price' 'Jason D. McEwen']"
] |
null | null | 2405.12969 | null | null | http://arxiv.org/pdf/2405.12969v1 | 2024-05-21T17:49:10Z | 2024-05-21T17:49:10Z | Can We Treat Noisy Labels as Accurate? | Noisy labels significantly hinder the accuracy and generalization of machine learning models, particularly due to ambiguous instance features. Traditional techniques that attempt to correct noisy labels directly, such as those using transition matrices, often fail to address the inherent complexities of the problem sufficiently. In this paper, we introduce EchoAlign, a transformative paradigm shift in learning from noisy labels. Instead of focusing on label correction, EchoAlign treats noisy labels ($tilde{Y}$) as accurate and modifies corresponding instance features ($X$) to achieve better alignment with $tilde{Y}$. EchoAlign's core components are (1) EchoMod: Employing controllable generative models, EchoMod precisely modifies instances while maintaining their intrinsic characteristics and ensuring alignment with the noisy labels. (2) EchoSelect: Instance modification inevitably introduces distribution shifts between training and test sets. EchoSelect maintains a significant portion of clean original instances to mitigate these shifts. It leverages the distinct feature similarity distributions between original and modified instances as a robust tool for accurate sample selection. This integrated approach yields remarkable results. In environments with 30% instance-dependent noise, even at 99% selection accuracy, EchoSelect retains nearly twice the number of samples compared to the previous best method. Notably, on three datasets, EchoAlign surpasses previous state-of-the-art techniques with a substantial improvement. | [
"['Yuxiang Zheng' 'Zhongyi Han' 'Yilong Yin' 'Xin Gao' 'Tongliang Liu']"
] |
null | null | 2405.12981 | null | null | http://arxiv.org/pdf/2405.12981v1 | 2024-05-21T17:59:29Z | 2024-05-21T17:59:29Z | Reducing Transformer Key-Value Cache Size with Cross-Layer Attention | Key-value (KV) caching plays an essential role in accelerating decoding for transformer-based autoregressive large language models (LLMs). However, the amount of memory required to store the KV cache can become prohibitive at long sequence lengths and large batch sizes. Since the invention of the transformer, two of the most effective interventions discovered for reducing the size of the KV cache have been Multi-Query Attention (MQA) and its generalization, Grouped-Query Attention (GQA). MQA and GQA both modify the design of the attention block so that multiple query heads can share a single key/value head, reducing the number of distinct key/value heads by a large factor while only minimally degrading accuracy. In this paper, we show that it is possible to take Multi-Query Attention a step further by also sharing key and value heads between adjacent layers, yielding a new attention design we call Cross-Layer Attention (CLA). With CLA, we find that it is possible to reduce the size of the KV cache by another 2x while maintaining nearly the same accuracy as unmodified MQA. In experiments training 1B- and 3B-parameter models from scratch, we demonstrate that CLA provides a Pareto improvement over the memory/accuracy tradeoffs which are possible with traditional MQA, enabling inference with longer sequence lengths and larger batch sizes than would otherwise be possible | [
"['William Brandon' 'Mayank Mishra' 'Aniruddha Nrusimha' 'Rameswar Panda'\n 'Jonathan Ragan Kelly']"
] |
null | null | 2405.13007 | null | null | http://arxiv.org/pdf/2405.13007v1 | 2024-05-13T08:53:43Z | 2024-05-13T08:53:43Z | News Recommendation with Category Description by a Large Language Model | Personalized news recommendations are essential for online news platforms to assist users in discovering news articles that match their interests from a vast amount of online content. Appropriately encoded content features, such as text, categories, and images, are essential for recommendations. Among these features, news categories, such as tv-golden-globe, finance-real-estate, and news-politics, play an important role in understanding news content, inspiring us to enhance the categories' descriptions. In this paper, we propose a novel method that automatically generates informative category descriptions using a large language model (LLM) without manual effort or domain-specific knowledge and incorporates them into recommendation models as additional information. In our comprehensive experimental evaluations using the MIND dataset, our method successfully achieved 5.8% improvement at most in AUC compared with baseline approaches without the LLM's generated category descriptions for the state-of-the-art content-based recommendation models including NAML, NRMS, and NPA. These results validate the effectiveness of our approach. The code is available at https://github.com/yamanalab/gpt-augmented-news-recommendation. | [
"['Yuki Yada' 'Hayato Yamana']"
] |
null | null | 2405.13016 | null | null | http://arxiv.org/pdf/2405.13016v1 | 2024-05-14T15:08:32Z | 2024-05-14T15:08:32Z | The Evolution of Darija Open Dataset: Introducing Version 2 | Darija Open Dataset (DODa) represents an open-source project aimed at enhancing Natural Language Processing capabilities for the Moroccan dialect, Darija. With approximately 100,000 entries, DODa stands as the largest collaborative project of its kind for Darija-English translation. The dataset features semantic and syntactic categorizations, variations in spelling, verb conjugations across multiple tenses, as well as tens of thousands of translated sentences. The dataset includes entries written in both Latin and Arabic alphabets, reflecting the linguistic variations and preferences found in different sources and applications. The availability of such dataset is critical for developing applications that can accurately understand and generate Darija, thus supporting the linguistic needs of the Moroccan community and potentially extending to similar dialects in neighboring regions. This paper explores the strategic importance of DODa, its current achievements, and the envisioned future enhancements that will continue to promote its use and expansion in the global NLP landscape. | [
"['Aissam Outchakoucht' 'Hamza Es-Samaali']"
] |
null | null | 2405.13017 | null | null | http://arxiv.org/pdf/2405.13017v1 | 2024-05-15T05:41:06Z | 2024-05-15T05:41:06Z | A Systematic Analysis on the Temporal Generalization of Language Models
in Social Media | In machine learning, temporal shifts occur when there are differences between training and test splits in terms of time. For streaming data such as news or social media, models are commonly trained on a fixed corpus from a certain period of time, and they can become obsolete due to the dynamism and evolving nature of online content. This paper focuses on temporal shifts in social media and, in particular, Twitter. We propose a unified evaluation scheme to assess the performance of language models (LMs) under temporal shift on standard social media tasks. LMs are tested on five diverse social media NLP tasks under different temporal settings, which revealed two important findings: (i) the decrease in performance under temporal shift is consistent across different models for entity-focused tasks such as named entity recognition or disambiguation, and hate speech detection, but not significant in the other tasks analysed (i.e., topic and sentiment classification); and (ii) continuous pre-training on the test period does not improve the temporal adaptability of LMs. | [
"['Asahi Ushio' 'Jose Camacho-Collados']"
] |
null | null | 2405.13022 | null | null | http://arxiv.org/pdf/2405.13022v2 | 2024-07-03T14:46:52Z | 2024-05-15T13:35:43Z | LLMs can learn self-restraint through iterative self-reflection | In order to be deployed safely, Large Language Models (LLMs) must be capable of dynamically adapting their behavior based on their level of knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-restraint, is non-trivial to teach since it depends on the internal knowledge of an LLM. By default, LLMs are trained to maximize the next token likelihood, which does not teach the model to modulate its answer based on its level of uncertainty. In order to learn self-restraint, we devise a utility function that can encourage the model to produce responses only when it is confident in them. This utility function can be used to score generation of different length and abstention. To optimize this function, we introduce ReSearch, a process of "self-reflection" consisting of iterative self-prompting and self-evaluation. We use the ReSearch algorithm to generate synthetic data on which we finetune our models. Compared to their original versions, our resulting models generate fewer emph{hallucinations} overall at no additional inference cost, for both known and unknown topics, as the model learns to selectively restrain itself. In addition, our method elegantly incorporates the ability to abstain by augmenting the samples generated by the model during the search procedure with an answer expressing abstention. | [
"['Alexandre Piché' 'Aristides Milios' 'Dzmitry Bahdanau' 'Chris Pal']"
] |
null | null | 2405.13031 | null | null | http://arxiv.org/pdf/2405.13031v1 | 2024-05-16T10:45:43Z | 2024-05-16T10:45:43Z | A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in
Text | In this work, a robust autoencoder ensemble-based approach designed to address anomaly detection in text corpora is introduced. Each autoencoder within the ensemble incorporates a local robust subspace recovery projection of the original data in its encoding embedding, leveraging the geometric properties of the k-nearest neighbors to optimize subspace recovery and identify anomalous patterns in textual data. The evaluation of such an approach needs an experimental setting dedicated to the context of textual anomaly detection. Thus, beforehand, a comprehensive real-world taxonomy is introduced to distinguish between independent anomalies and contextual anomalies. Such a study to identify clearly the kinds of anomalies appearing in a textual context aims at addressing a critical gap in the existing literature. Then, extensive experiments on classical text corpora have been conducted and their results are presented that highlights the efficiency, both in robustness and in performance, of the robust autoencoder ensemble-based approach when detecting both independent and contextual anomalies. Diverse range of tasks, including classification, sentiment analysis, and spam detection, across eight different corpora, have been studied in these experiments. | [
"['Jeremie Pantin' 'Christophe Marsala']"
] |
null | null | 2405.13044 | null | null | http://arxiv.org/pdf/2405.13044v1 | 2024-05-18T10:06:55Z | 2024-05-18T10:06:55Z | Case-Based Reasoning Approach for Solving Financial Question Answering | Measuring a machine's understanding of human language often involves assessing its reasoning skills, i.e. logical process of deriving answers to questions. While recent language models have shown remarkable proficiency in text based tasks, their efficacy in complex reasoning problems involving heterogeneous information such as text, tables, and numbers remain uncertain. Addressing this gap, FinQA introduced a numerical reasoning dataset for financial documents and simultaneously proposed a program generation approach . Our investigation reveals that half of the errors (48%) stem from incorrect operations being generated. To address this issue, we propose a novel approach to tackle numerical reasoning problems using case based reasoning (CBR), an artificial intelligence paradigm that provides problem solving guidance by offering similar cases (i.e. similar questions and corresponding logical programs). Our model retrieves relevant cases to address a given question, and then generates an answer based on the retrieved cases and contextual information. Through experiments on the FinQA dataset, we demonstrate competitive performance of our approach and additionally show that by expanding case repository, we can help solving complex multi step programs which FinQA showed weakness of. | [
"['Yikyung Kim' 'Jay-Yoon Lee']"
] |
null | null | 2405.13046 | null | null | http://arxiv.org/pdf/2405.13046v1 | 2024-05-18T22:23:07Z | 2024-05-18T22:23:07Z | LeaPformer: Enabling Linear Transformers for Autoregressive and
Simultaneous Tasks via Learned Proportions | A promising approach to preserving model performance in linearized transformers is to employ position-based re-weighting functions. However, state-of-the-art re-weighting functions rely heavily on target sequence lengths, making it difficult or impossible to apply them to autoregressive and simultaneous tasks, where the target and sometimes even the input sequence length are unknown. To address this issue, we propose Learned Proportions (LeaP) and LeaPformers. Our contribution is built on two major components. First, we generalize the dependence on explicit positional representations and sequence lengths into dependence on sequence proportions for re-weighting. Second, we replace static positional representations with dynamic proportions derived via a compact module, enabling more flexible attention concentration patterns. We evaluate LeaPformer against eight representative efficient transformers on the Long-Range Arena benchmark, showing that LeaPformer achieves the best quality-throughput trade-off, as well as LeaPformer to Wikitext-103 autoregressive language modeling and simultaneous speech-to-text translation for two language pairs, achieving competitive results. | [
"['Victor Agostinelli' 'Sanghyun Hong' 'Lizhong Chen']"
] |
null | null | 2405.13052 | null | null | http://arxiv.org/pdf/2405.13052v1 | 2024-05-19T20:33:36Z | 2024-05-19T20:33:36Z | Large Language Models Can Infer Personality from Free-Form User
Interactions | This study investigates the capacity of Large Language Models (LLMs) to infer the Big Five personality traits from free-form user interactions. The results demonstrate that a chatbot powered by GPT-4 can infer personality with moderate accuracy, outperforming previous approaches drawing inferences from static text content. The accuracy of inferences varied across different conversational settings. Performance was highest when the chatbot was prompted to elicit personality-relevant information from users (mean r=.443, range=[.245, .640]), followed by a condition placing greater emphasis on naturalistic interaction (mean r=.218, range=[.066, .373]). Notably, the direct focus on personality assessment did not result in a less positive user experience, with participants reporting the interactions to be equally natural, pleasant, engaging, and humanlike across both conditions. A chatbot mimicking ChatGPT's default behavior of acting as a helpful assistant led to markedly inferior personality inferences and lower user experience ratings but still captured psychologically meaningful information for some of the personality traits (mean r=.117, range=[-.004, .209]). Preliminary analyses suggest that the accuracy of personality inferences varies only marginally across different socio-demographic subgroups. Our results highlight the potential of LLMs for psychological profiling based on conversational interactions. We discuss practical implications and ethical challenges associated with these findings. | [
"['Heinrich Peters' 'Moran Cerf' 'Sandra C. Matz']"
] |
null | null | 2405.13058 | null | null | http://arxiv.org/abs/2405.13058v2 | 2024-06-05T15:28:43Z | 2024-05-20T11:10:49Z | The AI Community Building the Future? A Quantitative Analysis of
Development Activity on Hugging Face Hub | Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in the open AI ecosystem. This paper responds to this gap with a three-part quantitative analysis of development activity on the Hugging Face (HF) Hub, a popular platform for building, sharing, and demonstrating models. First, various types of activity across 348,181 model, 65,761 dataset, and 156,642 space repositories exhibit right-skewed distributions. Activity is extremely imbalanced between repositories; for example, over 70% of models have 0 downloads, while 1% account for 99% of downloads. Furthermore, licenses matter: there are statistically significant differences in collaboration patterns in model repositories with permissive, restrictive, and no licenses. Second, we analyse a snapshot of the social network structure of collaboration in model repositories, finding that the community has a core-periphery structure, with a core of prolific developers and a majority of isolate developers (89%). Upon removing the isolate developers from the network, collaboration is characterised by high reciprocity regardless of developers' network positions. Third, we examine model adoption through the lens of model usage in spaces, finding that a minority of models, developed by a handful of companies, are widely used on the HF Hub. Overall, activity on the HF Hub is characterised by Pareto distributions, congruent with OSS development patterns on platforms like GitHub. We conclude with recommendations for researchers, companies, and policymakers to advance our understanding of open AI development. | [
"['Cailean Osborne' 'Jennifer Ding' 'Hannah Rose Kirk']"
] |
null | null | 2405.13062 | null | null | http://arxiv.org/pdf/2405.13062v1 | 2024-05-20T14:41:59Z | 2024-05-20T14:41:59Z | StatAvg: Mitigating Data Heterogeneity in Federated Learning for
Intrusion Detection Systems | Federated learning (FL) is a decentralized learning technique that enables participating devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model without revealing their raw data to a third party. Due to its privacy-preserving nature, FL has sparked widespread attention for building Intrusion Detection Systems (IDS) within the realm of cybersecurity. However, the data heterogeneity across participating domains and entities presents significant challenges for the reliable implementation of an FL-based IDS. In this paper, we propose an effective method called Statistical Averaging (StatAvg) to alleviate non-independently and identically (non-iid) distributed features across local clients' data in FL. In particular, StatAvg allows the FL clients to share their individual data statistics with the server, which then aggregates this information to produce global statistics. The latter are shared with the clients and used for universal data normalisation. It is worth mentioning that StatAvg can seamlessly integrate with any FL aggregation strategy, as it occurs before the actual FL training process. The proposed method is evaluated against baseline approaches using datasets for network and host Artificial Intelligence (AI)-powered IDS. The experimental results demonstrate the efficiency of StatAvg in mitigating non-iid feature distributions across the FL clients compared to the baseline methods. | [
"['Pavlos S. Bouzinis' 'Panagiotis Radoglou-Grammatikis' 'Ioannis Makris'\n 'Thomas Lagkas' 'Vasileios Argyriou' 'Georgios Th. Papadopoulos'\n 'Panagiotis Sarigiannidis' 'George K. Karagiannidis']"
] |
null | null | 2405.13063 | null | null | http://arxiv.org/pdf/2405.13063v2 | 2024-05-28T16:03:20Z | 2024-05-20T14:45:18Z | Aurora: A Foundation Model of the Atmosphere | Deep learning foundation models are revolutionizing many facets of science by leveraging vast amounts of data to learn general-purpose representations that can be adapted to tackle diverse downstream tasks. Foundation models hold the promise to also transform our ability to model our planet and its subsystems by exploiting the vast expanse of Earth system data. Here we introduce Aurora, a large-scale foundation model of the atmosphere trained on over a million hours of diverse weather and climate data. Aurora leverages the strengths of the foundation modelling approach to produce operational forecasts for a wide variety of atmospheric prediction problems, including those with limited training data, heterogeneous variables, and extreme events. In under a minute, Aurora produces 5-day global air pollution predictions and 10-day high-resolution weather forecasts that outperform state-of-the-art classical simulation tools and the best specialized deep learning models. Taken together, these results indicate that foundation models can transform environmental forecasting. | [
"['Cristian Bodnar' 'Wessel P. Bruinsma' 'Ana Lucic' 'Megan Stanley'\n 'Johannes Brandstetter' 'Patrick Garvan' 'Maik Riechert' 'Jonathan Weyn'\n 'Haiyu Dong' 'Anna Vaughan' 'Jayesh K. Gupta' 'Kit Tambiratnam'\n 'Alex Archibald' 'Elizabeth Heider' 'Max Welling' 'Richard E. Turner'\n 'Paris Perdikaris']"
] |
null | null | 2405.13066 | null | null | http://arxiv.org/pdf/2405.13066v1 | 2024-05-20T16:14:39Z | 2024-05-20T16:14:39Z | Practical Performance of a Distributed Processing Framework for
Machine-Learning-based NIDS | Network Intrusion Detection Systems (NIDSs) detect intrusion attacks in network traffic. In particular, machine-learning-based NIDSs have attracted attention because of their high detection rates of unknown attacks. A distributed processing framework for machine-learning-based NIDSs employing a scalable distributed stream processing system has been proposed in the literature. However, its performance, when machine-learning-based classifiers are implemented has not been comprehensively evaluated. In this study, we implement five representative classifiers (Decision Tree, Random Forest, Naive Bayes, SVM, and kNN) based on this framework and evaluate their throughput and latency. By conducting the experimental measurements, we investigate the difference in the processing performance among these classifiers and the bottlenecks in the processing performance of the framework. | [
"['Maho Kajiura' 'Junya Nakamura']"
] |
null | null | 2405.13068 | null | null | http://arxiv.org/pdf/2405.13068v2 | 2024-06-19T13:51:06Z | 2024-05-20T17:17:55Z | Lockpicking LLMs: A Logit-Based Jailbreak Using Token-level Manipulation | Large language models (LLMs) have transformed the field of natural language processing, but they remain susceptible to jailbreaking attacks that exploit their capabilities to generate unintended and potentially harmful content. Existing token-level jailbreaking techniques, while effective, face scalability and efficiency challenges, especially as models undergo frequent updates and incorporate advanced defensive measures. In this paper, we introduce JailMine, an innovative token-level manipulation approach that addresses these limitations effectively. JailMine employs an automated "mining" process to elicit malicious responses from LLMs by strategically selecting affirmative outputs and iteratively reducing the likelihood of rejection. Through rigorous testing across multiple well-known LLMs and datasets, we demonstrate JailMine's effectiveness and efficiency, achieving a significant average reduction of 86% in time consumed while maintaining high success rates averaging 95%, even in the face of evolving defensive strategies. Our work contributes to the ongoing effort to assess and mitigate the vulnerability of LLMs to jailbreaking attacks, underscoring the importance of continued vigilance and proactive measures to enhance the security and reliability of these powerful language models. | [
"['Yuxi Li' 'Yi Liu' 'Yuekang Li' 'Ling Shi' 'Gelei Deng' 'Shengquan Chen'\n 'Kailong Wang']"
] |
null | null | 2405.13073 | null | null | http://arxiv.org/pdf/2405.13073v1 | 2024-05-20T23:11:03Z | 2024-05-20T23:11:03Z | A graph-structured distance for heterogeneous datasets with meta
variables | Heterogeneous datasets emerge in various machine learning or optimization applications that feature different data sources, various data types and complex relationships between variables. In practice, heterogeneous datasets are often partitioned into smaller well-behaved ones that are easier to process. However, some applications involve expensive-to-generate or limited size datasets, which motivates methods based on the whole dataset. The first main contribution of this work is a modeling graph-structured framework that generalizes state-of-the-art hierarchical, tree-structured, or variable-size frameworks. This framework models domains that involve heterogeneous datasets in which variables may be continuous, integer, or categorical, with some identified as meta if their values determine the inclusion/exclusion or affect the bounds of other so-called decreed variables. Excluded variables are introduced to manage variables that are either included or excluded depending on the given points. The second main contribution is the graph-structured distance that compares extended points with any combination of included and excluded variables: any pair of points can be compared, allowing to work directly in heterogeneous datasets with meta variables. The contributions are illustrated with some regression experiments, in which the performance of a multilayer perceptron with respect to its hyperparameters is modeled with inverse distance weighting and $K$-nearest neighbors models. | [
"['Edward Hallé-Hannan' 'Charles Audet' 'Youssef Diouane'\n 'Sébastien Le Digabel' 'Paul Saves']"
] |
null | null | 2405.13075 | null | null | http://arxiv.org/pdf/2405.13075v1 | 2024-05-21T02:00:55Z | 2024-05-21T02:00:55Z | Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate
Time Series Imputation | Multivariant time series (MTS) data are usually incomplete in real scenarios, and imputing the incomplete MTS is practically important to facilitate various time series mining tasks. Recently, diffusion model-based MTS imputation methods have achieved promising results by utilizing CNN or attention mechanisms for temporal feature learning. However, it is hard to adaptively trade off the diverse effects of local and global temporal features by simply combining CNN and attention. To address this issue, we propose a Score-weighted Convolutional Diffusion Model (Score-CDM for short), whose backbone consists of a Score-weighted Convolution Module (SCM) and an Adaptive Reception Module (ARM). SCM adopts a score map to capture the global temporal features in the time domain, while ARM uses a Spectral2Time Window Block (S2TWB) to convolve the local time series data in the spectral domain. Benefiting from the time convolution properties of Fast Fourier Transformation, ARM can adaptively change the receptive field of the score map, and thus effectively balance the local and global temporal features. We conduct extensive evaluations on three real MTS datasets of different domains, and the result verifies the effectiveness of the proposed Score-CDM. | [
"['S. Zhang' 'S. Wang' 'H. Miao' 'H. Chen' 'C. Fan' 'J. Zhang']"
] |
null | null | 2405.13076 | null | null | http://arxiv.org/pdf/2405.13076v1 | 2024-05-21T02:24:46Z | 2024-05-21T02:24:46Z | A K-means Algorithm for Financial Market Risk Forecasting | Financial market risk forecasting involves applying mathematical models, historical data analysis and statistical methods to estimate the impact of future market movements on investments. This process is crucial for investors to develop strategies, financial institutions to manage assets and regulators to formulate policy. In today's society, there are problems of high error rate and low precision in financial market risk prediction, which greatly affect the accuracy of financial market risk prediction. K-means algorithm in machine learning is an effective risk prediction technique for financial market. This study uses K-means algorithm to develop a financial market risk prediction system, which significantly improves the accuracy and efficiency of financial market risk prediction. Ultimately, the outcomes of the experiments confirm that the K-means algorithm operates with user-friendly simplicity and achieves a 94.61% accuracy rate | [
"['Jinxin Xu' 'Kaixian Xu' 'Yue Wang' 'Qinyan Shen' 'Ruisi Li']"
] |
null | null | 2405.13078 | null | null | http://arxiv.org/pdf/2405.13078v1 | 2024-05-21T04:43:15Z | 2024-05-21T04:43:15Z | Exploring Dark Knowledge under Various Teacher Capacities and Addressing
Capacity Mismatch | Knowledge Distillation (KD) could transfer the ``dark knowledge" of a well-performed yet large neural network to a weaker but lightweight one. From the view of output logits and softened probabilities, this paper goes deeper into the dark knowledge provided by teachers with different capacities. Two fundamental observations are: (1) a larger teacher tends to produce probability vectors that are less distinct between non-ground-truth classes; (2) teachers with different capacities are basically consistent in their cognition of relative class affinity. Abundant experimental studies verify these observations and in-depth empirical explanations are provided. The difference in dark knowledge leads to the peculiar phenomenon named ``capacity mismatch" that a more accurate teacher does not necessarily perform as well as a smaller teacher when teaching the same student network. Enlarging the distinctness between non-ground-truth class probabilities for larger teachers could address the capacity mismatch problem. This paper explores multiple simple yet effective ways to achieve this goal and verify their success by comparing them with popular KD methods that solve the capacity mismatch. | [
"['Xin-Chun Li' 'Wen-Shu Fan' 'Bowen Tao' 'Le Gan' 'De-Chuan Zhan']"
] |
null | null | 2405.13080 | null | null | http://arxiv.org/pdf/2405.13080v1 | 2024-05-21T06:14:49Z | 2024-05-21T06:14:49Z | EmInspector: Combating Backdoor Attacks in Federated Self-Supervised
Learning Through Embedding Inspection | Federated self-supervised learning (FSSL) has recently emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data while preserving data privacy. While FSSL offers advantages, its susceptibility to backdoor attacks, a concern identified in traditional federated supervised learning (FSL), has not been investigated. To fill the research gap, we undertake a comprehensive investigation into a backdoor attack paradigm, where unscrupulous clients conspire to manipulate the global model, revealing the vulnerability of FSSL to such attacks. In FSL, backdoor attacks typically build a direct association between the backdoor trigger and the target label. In contrast, in FSSL, backdoor attacks aim to alter the global model's representation for images containing the attacker's specified trigger pattern in favor of the attacker's intended target class, which is less straightforward. In this sense, we demonstrate that existing defenses are insufficient to mitigate the investigated backdoor attacks in FSSL, thus finding an effective defense mechanism is urgent. To tackle this issue, we dive into the fundamental mechanism of backdoor attacks on FSSL, proposing the Embedding Inspector (EmInspector) that detects malicious clients by inspecting the embedding space of local models. In particular, EmInspector assesses the similarity of embeddings from different local models using a small set of inspection images (e.g., ten images of CIFAR100) without specific requirements on sample distribution or labels. We discover that embeddings from backdoored models tend to cluster together in the embedding space for a given inspection image. Evaluation results show that EmInspector can effectively mitigate backdoor attacks on FSSL across various adversary settings. Our code is avaliable at https://github.com/ShuchiWu/EmInspector. | [
"['Yuwen Qian' 'Shuchi Wu' 'Kang Wei' 'Ming Ding' 'Di Xiao' 'Tao Xiang'\n 'Chuan Ma' 'Song Guo']"
] |
null | null | 2405.13082 | null | null | http://arxiv.org/pdf/2405.13082v1 | 2024-05-21T06:44:40Z | 2024-05-21T06:44:40Z | A Survey of Artificial Intelligence in Gait-Based Neurodegenerative
Disease Diagnosis | Recent years have witnessed an increasing global population affected by neurodegenerative diseases (NDs), which traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification, opening a new avenue to facilitate faster and more cost-effective diagnosis of NDs. In this paper, we provide a comprehensive survey on recent progress of machine learning and deep learning based AI techniques applied to diagnosis of five typical NDs through gait. We provide an overview of the process of AI-assisted NDs diagnosis, and present a systematic taxonomy of existing gait data and AI models. Through an extensive review and analysis of 164 studies, we identify and discuss the challenges, potential solutions, and future directions in this field. Finally, we envision the prospective utilization of 3D skeleton data for human gait representation and the development of more efficient AI models for NDs diagnosis. We provide a public resource repository to track and facilitate developments in this emerging field: https://github.com/Kali-Hac/AI4NDD-Survey. | [
"['Haocong Rao' 'Minlin Zeng' 'Xuejiao Zhao' 'Chunyan Miao']"
] |
null | null | 2405.13088 | null | null | http://arxiv.org/pdf/2405.13088v1 | 2024-05-21T11:42:15Z | 2024-05-21T11:42:15Z | Combining Relevance and Magnitude for Resource-Aware DNN Pruning | Pruning neural networks, i.e., removing some of their parameters whilst retaining their accuracy, is one of the main ways to reduce the latency of a machine learning pipeline, especially in resource- and/or bandwidth-constrained scenarios. In this context, the pruning technique, i.e., how to choose the parameters to remove, is critical to the system performance. In this paper, we propose a novel pruning approach, called FlexRel and predicated upon combining training-time and inference-time information, namely, parameter magnitude and relevance, in order to improve the resulting accuracy whilst saving both computational resources and bandwidth. Our performance evaluation shows that FlexRel is able to achieve higher pruning factors, saving over 35% bandwidth for typical accuracy targets. | [
"['Carla Fabiana Chiasserini' 'Francesco Malandrino' 'Nuria Molner'\n 'Zhiqiang Zhao']"
] |
null | null | 2405.13089 | null | null | http://arxiv.org/pdf/2405.13089v3 | 2024-06-12T08:21:53Z | 2024-05-21T11:42:20Z | SEGAN: semi-supervised learning approach for missing data imputation | In many practical real-world applications, data missing is a very common phenomenon, making the development of data-driven artificial intelligence theory and technology increasingly difficult. Data completion is an important method for missing data preprocessing. Most existing miss-ing data completion models directly use the known information in the missing data set but ignore the impact of the data label information contained in the data set on the missing data completion model. To this end, this paper proposes a missing data completion model SEGAN based on semi-supervised learning, which mainly includes three important modules: generator, discriminator and classifier. In the SEGAN model, the classifier enables the generator to make more full use of known data and its label information when predicting missing data values. In addition, the SE-GAN model introduces a missing hint matrix to allow the discriminator to more effectively distinguish between known data and data filled by the generator. This paper theoretically proves that the SEGAN model that introduces a classifier and a missing hint matrix can learn the real known data distribution characteristics when reaching Nash equilibrium. Finally, a large number of experiments were conducted in this article, and the experimental results show that com-pared with the current state-of-the-art multivariate data completion method, the performance of the SEGAN model is improved by more than 3%. | [
"['Xiaohua Pan' 'Weifeng Wu' 'Peiran Liu' 'Zhen Li' 'Peng Lu' 'Peijian Cao'\n 'Jianfeng Zhang' 'Xianfei Qiu' 'YangYang Wu']"
] |
null | null | 2405.13090 | null | null | http://arxiv.org/pdf/2405.13090v1 | 2024-05-21T11:44:07Z | 2024-05-21T11:44:07Z | FedASTA: Federated adaptive spatial-temporal attention for traffic flow
prediction | Mobile devices and the Internet of Things (IoT) devices nowadays generate a large amount of heterogeneous spatial-temporal data. It remains a challenging problem to model the spatial-temporal dynamics under privacy concern. Federated learning (FL) has been proposed as a framework to enable model training across distributed devices without sharing original data which reduce privacy concern. Personalized federated learning (PFL) methods further address data heterogenous problem. However, these methods don't consider natural spatial relations among nodes. For the sake of modeling spatial relations, Graph Neural Netowork (GNN) based FL approach have been proposed. But dynamic spatial-temporal relations among edge nodes are not taken into account. Several approaches model spatial-temporal dynamics in a centralized environment, while less effort has been made under federated setting. To overcome these challeges, we propose a novel Federated Adaptive Spatial-Temporal Attention (FedASTA) framework to model the dynamic spatial-temporal relations. On the client node, FedASTA extracts temporal relations and trend patterns from the decomposed terms of original time series. Then, on the server node, FedASTA utilize trend patterns from clients to construct adaptive temporal-spatial aware graph which captures dynamic correlation between clients. Besides, we design a masked spatial attention module with both static graph and constructed adaptive graph to model spatial dependencies among clients. Extensive experiments on five real-world public traffic flow datasets demonstrate that our method achieves state-of-art performance in federated scenario. In addition, the experiments made in centralized setting show the effectiveness of our novel adaptive graph construction approach compared with other popular dynamic spatial-temporal aware methods. | [
"['Kaiyuan Li' 'Yihan Zhang' 'Xinlei Chen']"
] |
null | null | 2405.13092 | null | null | http://arxiv.org/pdf/2405.13092v1 | 2024-05-21T12:08:48Z | 2024-05-21T12:08:48Z | CausalPlayground: Addressing Data-Generation Requirements in
Cutting-Edge Causality Research | Research on causal effects often relies on synthetic data due to the scarcity of real-world datasets with ground-truth effects. Since current data-generating tools do not always meet all requirements for state-of-the-art research, ad-hoc methods are often employed. This leads to heterogeneity among datasets and delays research progress. We address the shortcomings of current data-generating libraries by introducing CausalPlayground, a Python library that provides a standardized platform for generating, sampling, and sharing structural causal models (SCMs). CausalPlayground offers fine-grained control over SCMs, interventions, and the generation of datasets of SCMs for learning and quantitative research. Furthermore, by integrating with Gymnasium, the standard framework for reinforcement learning (RL) environments, we enable online interaction with the SCMs. Overall, by introducing CausalPlayground we aim to foster more efficient and comparable research in the field. All code and API documentation is available at https://github.com/sa-and/CausalPlayground. | [
"['Andreas W M Sauter' 'Erman Acar' 'Aske Plaat']"
] |
null | null | 2405.13093 | null | null | http://arxiv.org/pdf/2405.13093v1 | 2024-05-21T12:57:10Z | 2024-05-21T12:57:10Z | Graph neural networks informed locally by thermodynamics | Thermodynamics-informed neural networks employ inductive biases for the enforcement of the first and second principles of thermodynamics. To construct these biases, a metriplectic evolution of the system is assumed. This provides excellent results, when compared to uninformed, black box networks. While the degree of accuracy can be increased in one or two orders of magnitude, in the case of graph networks, this requires assembling global Poisson and dissipation matrices, which breaks the local structure of such networks. In order to avoid this drawback, a local version of the metriplectic biases has been developed in this work, which avoids the aforementioned matrix assembly, thus preserving the node-by-node structure of the graph networks. We apply this framework for examples in the fields of solid and fluid mechanics. Our approach demonstrates significant computational efficiency and strong generalization capabilities, accurately making inferences on examples significantly different from those encountered during training. | [
"['Alicia Tierz' 'Iciar Alfaro' 'David González' 'Francisco Chinesta'\n 'Elías Cueto']"
] |
null | null | 2405.13094 | null | null | http://arxiv.org/pdf/2405.13094v1 | 2024-05-21T13:13:43Z | 2024-05-21T13:13:43Z | KPG: Key Propagation Graph Generator for Rumor Detection based on
Reinforcement Learning | The proliferation of rumors on social media platforms during significant events, such as the US elections and the COVID-19 pandemic, has a profound impact on social stability and public health. Existing approaches for rumor detection primarily rely on propagation graphs to enhance model effectiveness. However, the presence of noisy and irrelevant structures during the propagation process limits the efficacy of these approaches. To tackle this issue, techniques such as weight adjustment and data augmentation have been proposed. However, these techniques heavily depend on rich original propagation structures, thus hindering performance when dealing with rumors that lack sufficient propagation information in the early propagation stages. In this paper, we propose Key Propagation Graph Generator (KPG), a novel reinforcement learning-based rumor detection framework that generates contextually coherent and informative propagation patterns for events with insufficient topology information, while also identifies indicative substructures for events with redundant and noisy propagation structures. KPG consists of two key components: the Candidate Response Generator (CRG) and the Ending Node Selector (ENS). CRG learns the latent distribution from refined propagation patterns, filtering out noise and generating new candidates for ENS. Simultaneously, ENS identifies the most influential substructures within propagation graphs and generates training data for CRG. Moreover, we introduce an end-to-end framework that utilizes rewards to guide the entire training process via a pre-trained graph neural network. Extensive experiments conducted on four datasets demonstrate the superiority of our KPG compared to the state-of-the-art approaches. | [
"['Yusong Zhang' 'Kun Xie' 'Xingyi Zhang' 'Xiangyu Dong' 'Sibo Wang']"
] |
null | null | 2405.13102 | null | null | http://arxiv.org/pdf/2405.13102v1 | 2024-05-21T17:26:44Z | 2024-05-21T17:26:44Z | Trading Volume Maximization with Online Learning | We explore brokerage between traders in an online learning framework. At any round $t$, two traders meet to exchange an asset, provided the exchange is mutually beneficial. The broker proposes a trading price, and each trader tries to sell their asset or buy the asset from the other party, depending on whether the price is higher or lower than their private valuations. A trade happens if one trader is willing to sell and the other is willing to buy at the proposed price. Previous work provided guidance to a broker aiming at enhancing traders' total earnings by maximizing the gain from trade, defined as the sum of the traders' net utilities after each interaction. In contrast, we investigate how the broker should behave to maximize the trading volume, i.e., the total number of trades. We model the traders' valuations as an i.i.d. process with an unknown distribution. If the traders' valuations are revealed after each interaction (full-feedback), and the traders' valuations cumulative distribution function (cdf) is continuous, we provide an algorithm achieving logarithmic regret and show its optimality up to constant factors. If only their willingness to sell or buy at the proposed price is revealed after each interaction ($2$-bit feedback), we provide an algorithm achieving poly-logarithmic regret when the traders' valuations cdf is Lipschitz and show that this rate is near-optimal. We complement our results by analyzing the implications of dropping the regularity assumptions on the unknown traders' valuations cdf. If we drop the continuous cdf assumption, the regret rate degrades to $Theta(sqrt{T})$ in the full-feedback case, where $T$ is the time horizon. If we drop the Lipschitz cdf assumption, learning becomes impossible in the $2$-bit feedback case. | [
"['Tommaso Cesari' 'Roberto Colomboni']"
] |
null | null | 2405.13130 | null | null | http://arxiv.org/pdf/2405.13130v1 | 2024-05-21T18:02:47Z | 2024-05-21T18:02:47Z | Pure Planning to Pure Policies and In Between with a Recursive Tree
Planner | A recursive tree planner (RTP) is designed to function as a pure planner without policies at one extreme and run a pure greedy policy at the other. In between, the RTP exploits policies to improve planning performance and improve zero-shot transfer from one class of planning problem to another. Policies are learned through imitation of the planner. These are then used by the planner to improve policies in a virtuous cycle. To improve planning performance and zero-shot transfer, the RTP incorporates previously learned tasks as generalized actions (GA) at any level of its hierarchy, and can refine those GA by adding primitive actions at any level too. For search, the RTP uses a generalized Dijkstra algorithm [Dijkstra 1959] which tries the greedy policy first and then searches over near-greedy paths and then farther away as necessary. The RPT can return multiple sub-goals from lower levels as well as boundary states near obstacles, and can exploit policies with background and object-number invariance. Policies at all levels of the hierarchy can be learned simultaneously or in any order or come from outside the framework. The RTP is tested here on a variety of Box2d [Cato 2022] problems, including the classic lunar lander [Farama 2022], and on the MuJoCo [Todorov et al 2012] inverted pendulum. | [
"['A. Norman Redlich']"
] |
null | null | 2405.13135 | null | null | http://arxiv.org/abs/2405.13135v1 | 2024-05-21T18:12:37Z | 2024-05-21T18:12:37Z | Dataset Mention Extraction in Scientific Articles Using Bi-LSTM-CRF
Model | Datasets are critical for scientific research, playing an important role in replication, reproducibility, and efficiency. Researchers have recently shown that datasets are becoming more important for science to function properly, even serving as artifacts of study themselves. However, citing datasets is not a common or standard practice in spite of recent efforts by data repositories and funding agencies. This greatly affects our ability to track their usage and importance. A potential solution to this problem is to automatically extract dataset mentions from scientific articles. In this work, we propose to achieve such extraction by using a neural network based on a Bi-LSTM-CRF architecture. Our method achieves F1 = 0.885 in social science articles released as part of the Rich Context Dataset. We discuss the limitations of the current datasets and propose modifications to the model to be done in the future. | [
"['Tong Zeng' 'Daniel Acuna']"
] |
null | null | 2405.13136 | null | null | http://arxiv.org/pdf/2405.13136v2 | 2024-07-09T16:59:42Z | 2024-05-21T18:12:39Z | Towards Principled, Practical Policy Gradient for Bandits and Tabular
MDPs | We consider (stochastic) softmax policy gradient (PG) methods for bandits and tabular Markov decision processes (MDPs). While the PG objective is non-concave, recent research has used the objective's smoothness and gradient domination properties to achieve convergence to an optimal policy. However, these theoretical results require setting the algorithm parameters according to unknown problem-dependent quantities (e.g. the optimal action or the true reward vector in a bandit problem). To address this issue, we borrow ideas from the optimization literature to design practical, principled PG methods in both the exact and stochastic settings. In the exact setting, we employ an Armijo line-search to set the step-size for softmax PG and demonstrate a linear convergence rate. In the stochastic setting, we utilize exponentially decreasing step-sizes, and characterize the convergence rate of the resulting algorithm. We show that the proposed algorithm offers similar theoretical guarantees as the state-of-the art results, but does not require the knowledge of oracle-like quantities. For the multi-armed bandit setting, our techniques result in a theoretically-principled PG algorithm that does not require explicit exploration, the knowledge of the reward gap, the reward distributions, or the noise. Finally, we empirically compare the proposed methods to PG approaches that require oracle knowledge, and demonstrate competitive performance. | [
"['Michael Lu' 'Matin Aghaei' 'Anant Raj' 'Sharan Vaswani']"
] |
null | null | 2405.13140 | null | null | http://arxiv.org/pdf/2405.13140v2 | 2024-05-26T17:40:30Z | 2024-05-21T18:22:44Z | On Convergence of the Alternating Directions SGHMC Algorithm | We study convergence rates of Hamiltonian Monte Carlo (HMC) algorithms with leapfrog integration under mild conditions on stochastic gradient oracle for the target distribution (SGHMC). Our method extends standard HMC by allowing the use of general auxiliary distributions, which is achieved by a novel procedure of Alternating Directions. The convergence analysis is based on the investigations of the Dirichlet forms associated with the underlying Markov chain driving the algorithms. For this purpose, we provide a detailed analysis on the error of the leapfrog integrator for Hamiltonian motions with both the kinetic and potential energy functions in general form. We characterize the explicit dependence of the convergence rates on key parameters such as the problem dimension, functional properties of both the target and auxiliary distributions, and the quality of the oracle. | [
"['Soumyadip Ghosh' 'Yingdong Lu' 'Tomasz Nowicki']"
] |
null | null | 2405.13147 | null | null | http://arxiv.org/pdf/2405.13147v2 | 2024-06-07T19:56:12Z | 2024-05-21T18:34:14Z | A novel reliability attack of Physical Unclonable Functions | Physical Unclonable Functions (PUFs) are emerging as promising security primitives for IoT devices, providing device fingerprints based on physical characteristics. Despite their strengths, PUFs are vulnerable to machine learning (ML) attacks, including conventional and reliability-based attacks. Conventional ML attacks have been effective in revealing vulnerabilities of many PUFs, and reliability-based ML attacks are more powerful tools that have detected vulnerabilities of some PUFs that are resistant to conventional ML attacks. Since reliability-based ML attacks leverage information of PUFs' unreliability, we were tempted to examine the feasibility of building defense using reliability enhancing techniques, and have discovered that majority voting with reasonably high repeats provides effective defense against existing reliability-based ML attack methods. It is known that majority voting reduces but does not eliminate unreliability, we are motivated to investigate if new attack methods exist that can capture the low unreliability of highly but not-perfectly reliable PUFs, which led to the development of a new reliability representation and the new representation-enabled attack method that has experimentally cracked PUFs enhanced with majority voting of high repetitions. | [
"['Gaoxiang Li' 'Yu Zhuang']"
] |
null | null | 2405.13149 | null | null | http://arxiv.org/pdf/2405.13149v1 | 2024-05-21T18:38:14Z | 2024-05-21T18:38:14Z | Gaussian Measures Conditioned on Nonlinear Observations: Consistency,
MAP Estimators, and Simulation | The article presents a systematic study of the problem of conditioning a Gaussian random variable $xi$ on nonlinear observations of the form $F circ phi(xi)$ where $phi: mathcal{X} to mathbb{R}^N$ is a bounded linear operator and $F$ is nonlinear. Such problems arise in the context of Bayesian inference and recent machine learning-inspired PDE solvers. We give a representer theorem for the conditioned random variable $xi mid Fcirc phi(xi)$, stating that it decomposes as the sum of an infinite-dimensional Gaussian (which is identified analytically) as well as a finite-dimensional non-Gaussian measure. We also introduce a novel notion of the mode of a conditional measure by taking the limit of the natural relaxation of the problem, to which we can apply the existing notion of maximum a posteriori estimators of posterior measures. Finally, we introduce a variant of the Laplace approximation for the efficient simulation of the aforementioned conditioned Gaussian random variables towards uncertainty quantification. | [
"['Yifan Chen' 'Bamdad Hosseini' 'Houman Owhadi' 'Andrew M Stuart']"
] |
null | null | 2405.13155 | null | null | http://arxiv.org/pdf/2405.13155v1 | 2024-05-21T18:50:51Z | 2024-05-21T18:50:51Z | ReALLM: A general framework for LLM compression and fine-tuning | We introduce ReALLM, a novel approach for compression and memory-efficient adaptation of pre-trained language models that encompasses most of the post-training quantization and fine-tuning methods for a budget of <4 bits. Pre-trained matrices are decomposed into a high-precision low-rank component and a vector-quantized latent representation (using an autoencoder). During the fine-tuning step, only the low-rank components are updated. Our results show that pre-trained matrices exhibit different patterns. ReALLM adapts the shape of the encoder (small/large embedding, high/low bit VQ, etc.) to each matrix. ReALLM proposes to represent each matrix with a small embedding on $b$ bits and a neural decoder model $mathcal{D}_phi$ with its weights on $b_phi$ bits. The decompression of a matrix requires only one embedding and a single forward pass with the decoder. Our weight-only quantization algorithm yields the best results on language generation tasks (C4 and WikiText-2) for a budget of $3$ bits without any training. With a budget of $2$ bits, ReALLM achieves state-of-the art performance after fine-tuning on a small calibration dataset. | [
"['Louis Leconte' 'Lisa Bedin' 'Van Minh Nguyen' 'Eric Moulines']"
] |
null | null | 2405.13160 | null | null | http://arxiv.org/pdf/2405.13160v1 | 2024-05-21T19:03:09Z | 2024-05-21T19:03:09Z | Borrowing Strength in Distributionally Robust Optimization via
Hierarchical Dirichlet Processes | This paper presents a novel optimization framework to address key challenges presented by modern machine learning applications: High dimensionality, distributional uncertainty, and data heterogeneity. Our approach unifies regularized estimation, distributionally robust optimization (DRO), and hierarchical Bayesian modeling in a single data-driven criterion. By employing a hierarchical Dirichlet process (HDP) prior, the method effectively handles multi-source data, achieving regularization, distributional robustness, and borrowing strength across diverse yet related data-generating processes. We demonstrate the method's advantages by establishing theoretical performance guarantees and tractable Monte Carlo approximations based on Dirichlet process (DP) theory. Numerical experiments validate the framework's efficacy in improving and stabilizing both prediction and parameter estimation accuracy, showcasing its potential for application in complex data environments. | [
"['Nicola Bariletto' 'Khai Nguyen' 'Nhat Ho']"
] |
null | null | 2405.13173 | null | null | http://arxiv.org/pdf/2405.13173v1 | 2024-05-21T19:46:35Z | 2024-05-21T19:46:35Z | Efficient and Interpretable Information Retrieval for Product Question
Answering with Heterogeneous Data | Expansion-enhanced sparse lexical representation improves information retrieval (IR) by minimizing vocabulary mismatch problems during lexical matching. In this paper, we explore the potential of jointly learning dense semantic representation and combining it with the lexical one for ranking candidate information. We present a hybrid information retrieval mechanism that maximizes lexical and semantic matching while minimizing their shortcomings. Our architecture consists of dual hybrid encoders that independently encode queries and information elements. Each encoder jointly learns a dense semantic representation and a sparse lexical representation augmented by a learnable term expansion of the corresponding text through contrastive learning. We demonstrate the efficacy of our model in single-stage ranking of a benchmark product question-answering dataset containing the typical heterogeneous information available on online product pages. Our evaluation demonstrates that our hybrid approach outperforms independently trained retrievers by 10.95% (sparse) and 2.7% (dense) in MRR@5 score. Moreover, our model offers better interpretability and performs comparably to state-of-the-art cross encoders while reducing response time by 30% (latency) and cutting computational load by approximately 38% (FLOPs). | [
"['Biplob Biswas' 'Rajiv Ramnath']"
] |
null | null | 2405.13178 | null | null | http://arxiv.org/pdf/2405.13178v2 | 2024-06-05T01:11:10Z | 2024-05-21T20:01:03Z | One-Shot Imitation Learning with Invariance Matching for Robotic
Manipulation | Learning a single universal policy that can perform a diverse set of manipulation tasks is a promising new direction in robotics. However, existing techniques are limited to learning policies that can only perform tasks that are encountered during training, and require a large number of demonstrations to learn new tasks. Humans, on the other hand, often can learn a new task from a single unannotated demonstration. In this work, we propose the Invariance-Matching One-shot Policy Learning (IMOP) algorithm. In contrast to the standard practice of learning the end-effector's pose directly, IMOP first learns invariant regions of the state space for a given task, and then computes the end-effector's pose through matching the invariant regions between demonstrations and test scenes. Trained on the 18 RLBench tasks, IMOP achieves a success rate that outperforms the state-of-the-art consistently, by 4.5% on average over the 18 tasks. More importantly, IMOP can learn a novel task from a single unannotated demonstration, and without any fine-tuning, and achieves an average success rate improvement of $11.5%$ over the state-of-the-art on 22 novel tasks selected across nine categories. IMOP can also generalize to new shapes and learn to manipulate objects that are different from those in the demonstration. Further, IMOP can perform one-shot sim-to-real transfer using a single real-robot demonstration. | [
"['Xinyu Zhang' 'Abdeslam Boularias']"
] |
null | null | 2405.13180 | null | null | http://arxiv.org/pdf/2405.13180v1 | 2024-05-21T20:06:12Z | 2024-05-21T20:06:12Z | Data Assimilation with Machine Learning Surrogate Models: A Case Study
with FourCastNet | Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts. This paper investigates online weather prediction using machine learning surrogates supplemented with partial and noisy observations. We empirically demonstrate and theoretically justify that, despite the long-time instability of the surrogates and the sparsity of the observations, filtering estimates can remain accurate in the long-time horizon. As a case study, we integrate FourCastNet, a state-of-the-art weather surrogate model, within a variational data assimilation framework using partial, noisy ERA5 data. Our results show that filtering estimates remain accurate over a year-long assimilation window and provide effective initial conditions for forecasting tasks, including extreme event prediction. | [
"['Melissa Adrian' 'Daniel Sanz-Alonso' 'Rebecca Willett']"
] |
null | null | 2405.13181 | null | null | http://arxiv.org/pdf/2405.13181v1 | 2024-05-21T20:08:52Z | 2024-05-21T20:08:52Z | Comparative Analysis of Different Efficient Fine Tuning Methods of Large
Language Models (LLMs) in Low-Resource Setting | In the domain of large language models (LLMs), arXiv:2305.16938 showed that few-shot full-model fine-tuning -- namely Vanilla Fine Tuning (FT) and Pattern-Based Fine Tuning (PBFT) --, and In-Context Learning (ICL) generalize similarly on Out-Of-Domain (OOD) datasets, but vary in terms of task adaptation. However, they both pose challenges, especially in term of memory requirements. In this paper, we further try to push the understanding of different fine-tuning strategies for LLM and aim to bring a myriad of these on the same pedestal for an elaborate comparison with full-model fine-tuning on two diverse datasets. To that end, we conducted a series of experiments, beginning with state-of-the-art methods like vanilla fine-tuning and Pattern-Based Fine-Tuning (PBFT) on pre-trained models across two datasets, COLA and MNLI. We then investigate adaptive fine-tuning and the efficiency of LoRA adapters in a few-shot setting. Finally, we also compare an alternative approach that has gained recent popularity -- context distillation -- with the vanilla FT and PBFT with and without few-shot setup. Our findings suggest that these alternative strategies that we explored can exhibit out-of-domain generalization comparable to that of vanilla FT and PBFT. PBFT under-performs Vanilla FT on out-of-domain (OOD) data, emphasizing the need for effective prompts. Further, our adaptive-fine tuning and LoRA experiments perform comparable or slightly worse than the standard fine-tunings as anticipated, since standard fine-tunings involve tuning the entire model. Finally, our context distillation experiments out-perform the standard fine-tuning methods. These findings underscore that eventually the choice of an appropriate fine-tuning method depends on the available resources (memory, compute, data) and task adaptability. | [
"['Krishna Prasad Varadarajan Srinivasan' 'Prasanth Gumpena'\n 'Madhusudhana Yattapu' 'Vishal H. Brahmbhatt']"
] |
null | null | 2405.13187 | null | null | http://arxiv.org/pdf/2405.13187v1 | 2024-05-21T20:31:42Z | 2024-05-21T20:31:42Z | A machine learning framework for interpretable predictions in patient
pathways: The case of predicting ICU admission for patients with symptoms of
sepsis | Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance - where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees - and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis. | [
"['Sandra Zilker' 'Sven Weinzierl' 'Mathias Kraus' 'Patrick Zschech'\n 'Martin Matzner']"
] |
null | null | 2405.13190 | null | null | http://arxiv.org/pdf/2405.13190v1 | 2024-05-21T20:37:07Z | 2024-05-21T20:37:07Z | Interpretable Spatio-Temporal Embedding for Brain Structural-Effective
Network with Ordinary Differential Equation | The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks via an ordinary differential equation (ODE) model, which characterizes spatial-temporal brain dynamics. Our framework is validated on several clinical phenotype prediction tasks using two independent publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods. | [
"['Haoteng Tang' 'Guodong Liu' 'Siyuan Dai' 'Kai Ye' 'Kun Zhao'\n 'Wenlu Wang' 'Carl Yang' 'Lifang He' 'Alex Leow' 'Paul Thompson'\n 'Heng Huang' 'Liang Zhan']"
] |
null | null | 2405.13191 | null | null | http://arxiv.org/pdf/2405.13191v1 | 2024-05-21T20:40:37Z | 2024-05-21T20:40:37Z | Pragmatic auditing: a pilot-driven approach for auditing Machine
Learning systems | The growing adoption and deployment of Machine Learning (ML) systems came with its share of ethical incidents and societal concerns. It also unveiled the necessity to properly audit these systems in light of ethical principles. For such a novel type of algorithmic auditing to become standard practice, two main prerequisites need to be available: A lifecycle model that is tailored towards transparency and accountability, and a principled risk assessment procedure that allows the proper scoping of the audit. Aiming to make a pragmatic step towards a wider adoption of ML auditing, we present a respective procedure that extends the AI-HLEG guidelines published by the European Commission. Our audit procedure is based on an ML lifecycle model that explicitly focuses on documentation, accountability, and quality assurance; and serves as a common ground for alignment between the auditors and the audited organisation. We describe two pilots conducted on real-world use cases from two different organisations and discuss the shortcomings of ML algorithmic auditing as well as future directions thereof. | [
"['Djalel Benbouzid' 'Christiane Plociennik' 'Laura Lucaj' 'Mihai Maftei'\n 'Iris Merget' 'Aljoscha Burchardt' 'Marc P. Hauer' 'Abdeldjallil Naceri'\n 'Patrick van der Smagt']"
] |
null | null | 2405.13193 | null | null | http://arxiv.org/pdf/2405.13193v1 | 2024-05-21T20:53:18Z | 2024-05-21T20:53:18Z | Efficient Imitation Learning with Conservative World Models | We tackle the problem of policy learning from expert demonstrations without a reward function. A central challenge in this space is that these policies fail upon deployment due to issues of distributional shift, environment stochasticity, or compounding errors. Adversarial imitation learning alleviates this issue but requires additional on-policy training samples for stability, which presents a challenge in realistic domains due to inefficient learning and high sample complexity. One approach to this issue is to learn a world model of the environment, and use synthetic data for policy training. While successful in prior works, we argue that this is sub-optimal due to additional distribution shifts between the learned model and the real environment. Instead, we re-frame imitation learning as a fine-tuning problem, rather than a pure reinforcement learning one. Drawing theoretical connections to offline RL and fine-tuning algorithms, we argue that standard online world model algorithms are not well suited to the imitation learning problem. We derive a principled conservative optimization bound and demonstrate empirically that it leads to improved performance on two very challenging manipulation environments from high-dimensional raw pixel observations. We set a new state-of-the-art performance on the Franka Kitchen environment from images, requiring only 10 demos on no reward labels, as well as solving a complex dexterity manipulation task. | [
"['Victor Kolev' 'Rafael Rafailov' 'Kyle Hatch' 'Jiajun Wu' 'Chelsea Finn']"
] |
null | null | 2405.13202 | null | null | http://arxiv.org/pdf/2405.13202v1 | 2024-05-21T21:12:09Z | 2024-05-21T21:12:09Z | Empowering Urban Traffic Management: Elevated 3D LiDAR for Data
Collection and Advanced Object Detection Analysis | The 3D object detection capabilities in urban environments have been enormously improved by recent developments in Light Detection and Range (LiDAR) technology. This paper presents a novel framework that transforms the detection and analysis of 3D objects in traffic scenarios by utilizing the power of elevated LiDAR sensors. We are presenting our methodology's remarkable capacity to collect complex 3D point cloud data, which allows us to accurately and in detail capture the dynamics of urban traffic. Due to the limitation in obtaining real-world traffic datasets, we utilize the simulator to generate 3D point cloud for specific scenarios. To support our experimental analysis, we firstly simulate various 3D point cloud traffic-related objects. Then, we use this dataset as a basis for training and evaluating our 3D object detection models, in identifying and monitoring both vehicles and pedestrians in simulated urban traffic environments. Next, we fine tune the Point Voxel-Region-based Convolutional Neural Network (PV-RCNN) architecture, making it more suited to handle and understand the massive volumes of point cloud data generated by our urban traffic simulations. Our results show the effectiveness of the proposed solution in accurately detecting objects in traffic scenes and highlight the role of LiDAR in improving urban safety and advancing intelligent transportation systems. | [
"['Nawfal Guefrachi' 'Hakim Ghazzai' 'Ahmad Alsharoa']"
] |
null | null | 2405.13203 | null | null | http://arxiv.org/pdf/2405.13203v1 | 2024-05-21T21:14:31Z | 2024-05-21T21:14:31Z | Modeling Real-Time Interactive Conversations as Timed Diarized
Transcripts | Chatbots built upon language models have exploded in popularity, but they have largely been limited to synchronous, turn-by-turn dialogues. In this paper we present a simple yet general method to simulate real-time interactive conversations using pretrained text-only language models, by modeling timed diarized transcripts and decoding them with causal rejection sampling. We demonstrate the promise of this method with two case studies: instant messenger dialogues and spoken conversations, which require generation at about 30 tok/s and 20 tok/s respectively to maintain real-time interactivity. These capabilities can be added into language models using relatively little data and run on commodity hardware. | [
"['Garrett Tanzer' 'Gustaf Ahdritz' 'Luke Melas-Kyriazi']"
] |
null | null | 2405.13205 | null | null | http://arxiv.org/pdf/2405.13205v2 | 2024-06-08T18:08:09Z | 2024-05-21T21:15:45Z | Multi-Agent Reinforcement Learning with Hierarchical Coordination for
Emergency Responder Stationing | An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition responders between predesignated waiting locations to cover any gaps that arise due to the prior dispatch of responders or significant changes in the distribution of anticipated requests. Optimal repositioning is computationally challenging due to the exponential number of ways to allocate responders between locations and the uncertainty in future requests. The state-of-the-art approach in proactive repositioning is a hierarchical approach based on spatial decomposition and online Monte Carlo tree search, which may require minutes of computation for each decision in a domain where seconds can save lives. We address the issue of long decision times by introducing a novel reinforcement learning (RL) approach, based on the same hierarchical decomposition, but replacing online search with learning. To address the computational challenges posed by large, variable-dimensional, and discrete state and action spaces, we propose: (1) actor-critic based agents that incorporate transformers to handle variable-dimensional states and actions, (2) projections to fixed-dimensional observations to handle complex states, and (3) combinatorial techniques to map continuous actions to discrete allocations. We evaluate our approach using real-world data from two U.S. cities, Nashville, TN and Seattle, WA. Our experiments show that compared to the state of the art, our approach reduces computation time per decision by three orders of magnitude, while also slightly reducing average ambulance response time by 5 seconds. | [
"['Amutheezan Sivagnanam' 'Ava Pettet' 'Hunter Lee' 'Ayan Mukhopadhyay'\n 'Abhishek Dubey' 'Aron Laszka']"
] |
null | null | 2405.13209 | null | null | http://arxiv.org/pdf/2405.13209v1 | 2024-05-21T21:24:34Z | 2024-05-21T21:24:34Z | Investigating Symbolic Capabilities of Large Language Models | Prompting techniques have significantly enhanced the capabilities of Large Language Models (LLMs) across various complex tasks, including reasoning, planning, and solving math word problems. However, most research has predominantly focused on language-based reasoning and word problems, often overlooking the potential of LLMs in handling symbol-based calculations and reasoning. This study aims to bridge this gap by rigorously evaluating LLMs on a series of symbolic tasks, such as addition, multiplication, modulus arithmetic, numerical precision, and symbolic counting. Our analysis encompasses eight LLMs, including four enterprise-grade and four open-source models, of which three have been pre-trained on mathematical tasks. The assessment framework is anchored in Chomsky's Hierarchy, providing a robust measure of the computational abilities of these models. The evaluation employs minimally explained prompts alongside the zero-shot Chain of Thoughts technique, allowing models to navigate the solution process autonomously. The findings reveal a significant decline in LLMs' performance on context-free and context-sensitive symbolic tasks as the complexity, represented by the number of symbols, increases. Notably, even the fine-tuned GPT3.5 exhibits only marginal improvements, mirroring the performance trends observed in other models. Across the board, all models demonstrated a limited generalization ability on these symbol-intensive tasks. This research underscores LLMs' challenges with increasing symbolic complexity and highlights the need for specialized training, memory and architectural adjustments to enhance their proficiency in symbol-based reasoning tasks. | [
"['Neisarg Dave' 'Daniel Kifer' 'C. Lee Giles' 'Ankur Mali']"
] |
null | null | 2405.13217 | null | null | http://arxiv.org/pdf/2405.13217v1 | 2024-05-21T21:42:49Z | 2024-05-21T21:42:49Z | Interactive Simulations of Backdoors in Neural Networks | This work addresses the problem of planting and defending cryptographic-based backdoors in artificial intelligence (AI) models. The motivation comes from our lack of understanding and the implications of using cryptographic techniques for planting undetectable backdoors under theoretical assumptions in the large AI model systems deployed in practice. Our approach is based on designing a web-based simulation playground that enables planting, activating, and defending cryptographic backdoors in neural networks (NN). Simulations of planting and activating backdoors are enabled for two scenarios: in the extension of NN model architecture to support digital signature verification and in the modified architectural block for non-linear operators. Simulations of backdoor defense against backdoors are available based on proximity analysis and provide a playground for a game of planting and defending against backdoors. The simulations are available at https://pages.nist.gov/nn-calculator | [
"['Peter Bajcsy' 'Maxime Bros']"
] |
null | null | 2405.13220 | null | null | http://arxiv.org/pdf/2405.13220v1 | 2024-05-21T22:00:34Z | 2024-05-21T22:00:34Z | Paired Autoencoders for Inverse Problems | We consider the solution of nonlinear inverse problems where the forward problem is a discretization of a partial differential equation. Such problems are notoriously difficult to solve in practice and require minimizing a combination of a data-fit term and a regularization term. The main computational bottleneck of typical algorithms is the direct estimation of the data misfit. Therefore, likelihood-free approaches have become appealing alternatives. Nonetheless, difficulties in generalization and limitations in accuracy have hindered their broader utility and applicability. In this work, we use a paired autoencoder framework as a likelihood-free estimator for inverse problems. We show that the use of such an architecture allows us to construct a solution efficiently and to overcome some known open problems when using likelihood-free estimators. In particular, our framework can assess the quality of the solution and improve on it if needed. We demonstrate the viability of our approach using examples from full waveform inversion and inverse electromagnetic imaging. | [
"['Matthias Chung' 'Emma Hart' 'Julianne Chung' 'Bas Peters' 'Eldad Haber']"
] |
null | null | 2405.13226 | null | null | http://arxiv.org/pdf/2405.13226v1 | 2024-05-21T22:26:01Z | 2024-05-21T22:26:01Z | Dataset Decomposition: Faster LLM Training with Variable Sequence Length
Curriculum | Large language models (LLMs) are commonly trained on datasets consisting of fixed-length token sequences. These datasets are created by randomly concatenating documents of various lengths and then chunking them into sequences of a predetermined target length. However, this method of concatenation can lead to cross-document attention within a sequence, which is neither a desirable learning signal nor computationally efficient. Additionally, training on long sequences becomes computationally prohibitive due to the quadratic cost of attention. In this study, we introduce dataset decomposition, a novel variable sequence length training technique, to tackle these challenges. We decompose a dataset into a union of buckets, each containing sequences of the same size extracted from a unique document. During training, we use variable sequence length and batch size, sampling simultaneously from all buckets with a curriculum. In contrast to the concat-and-chunk baseline, which incurs a fixed attention cost at every step of training, our proposed method incurs a penalty proportional to the actual document lengths at each step, resulting in significant savings in training time. We train an 8k context-length 1B model at the same cost as a 2k context-length model trained with the baseline approach. Experiments on a web-scale corpus demonstrate that our approach significantly enhances performance on standard language evaluations and long-context benchmarks, reaching target accuracy 3x faster compared to the baseline. Our method not only enables efficient pretraining on long sequences but also scales effectively with dataset size. Lastly, we shed light on a critical yet less studied aspect of training large language models: the distribution and curriculum of sequence lengths, which results in a non-negligible difference in performance. | [
"['Hadi Pouransari' 'Chun-Liang Li' 'Jen-Hao Rick Chang'\n 'Pavan Kumar Anasosalu Vasu' 'Cem Koc' 'Vaishaal Shankar' 'Oncel Tuzel']"
] |
null | null | 2405.13227 | null | null | http://arxiv.org/pdf/2405.13227v1 | 2024-05-21T22:28:41Z | 2024-05-21T22:28:41Z | A rapid approach to urban traffic noise mapping with a generative
adversarial network | With rapid urbanisation and the accompanying increase in traffic density, traffic noise has become a major concern in urban planning. However, traditional grid noise mapping methods have limitations in terms of time consumption, software costs, and a lack of parameter integration interfaces. These limitations hinder their ability to meet the need for iterative updates and rapid performance feedback in the early design stages of street-scale urban planning. Herein, we developed a rapid urban traffic noise mapping technique that leverages generative adversarial networks (GANs) as a surrogate model. This approach enables the rapid assessment of urban traffic noise distribution by using urban elements such as roads and buildings as the input. The mean values for the mean squared error (MSE) and structural similarity index (SSIM) are 0.0949 and 0.8528, respectively, for the validation dataset. Hence, our prediction accuracy is on par with that of conventional prediction software. Furthermore, the trained model is integrated into Grasshopper as a tool, facilitating the rapid generation of traffic noise maps. This integration allows urban designers and planners, even those without expertise in acoustics, to easily anticipate changes in acoustics impacts caused by design. | [
"['Xinhao Yang' 'Zhen Han' 'Xiaodong Lu' 'Yuan Zhang']"
] |
null | null | 2405.13235 | null | null | http://arxiv.org/pdf/2405.13235v2 | 2024-07-07T16:54:09Z | 2024-05-21T22:42:08Z | Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose
Prediction from Freehand 2D Ultrasound Videos | Accurately localizing two-dimensional (2D) ultrasound (US) fetal brain images in the 3D brain, using minimal computational resources, is an important task for automated US analysis of fetal growth and development. We propose an uncertainty-aware deep learning model for automated 3D plane localization in 2D fetal brain images. Specifically, a multi-head network is trained to jointly regress 3D plane pose from 2D images in terms of different geometric transformations. The model explicitly learns to predict uncertainty to allocate higher weight to inputs with low variances across different transformations to improve performance. Our proposed method, QAERTS, demonstrates superior pose estimation accuracy than the state-of-the-art and most of the uncertainty-based approaches, leading to 9% improvement on plane angle (PA) for localization accuracy, and 8% on normalized cross-correlation (NCC) for sampled image quality. QAERTS also demonstrates efficiency, containing 5$times$ fewer parameters than ensemble-based approach, making it advantageous in resource-constrained settings. In addition, QAERTS proves to be more robust to noise effects observed in freehand US scanning by leveraging rotational discontinuities and explicit output uncertainties. | [
"['Jayroop Ramesh' 'Nicola K Dinsdale' 'the INTERGROWTH-21st Consortium'\n 'Pak-Hei Yeung' 'Ana IL Namburete']"
] |
null | null | 2405.13238 | null | null | http://arxiv.org/pdf/2405.13238v2 | 2024-05-26T23:18:53Z | 2024-05-21T22:53:00Z | Enhancing User Interest based on Stream Clustering and Memory Networks
in Large-Scale Recommender Systems | Recommender Systems (RSs) provide personalized recommendation service based on user interest, which are widely used in various platforms. However, there are lots of users with sparse interest due to lacking consumption behaviors, which leads to poor recommendation results for them. This problem is widespread in large-scale RSs and is particularly difficult to address. To solve this problem, we propose a novel solution named User Interest Enhancement (UIE) which enhances user interest including user profile and user history behavior sequences using the enhancement vectors and personalized enhancement vector generated based on stream clustering and memory networks from different perspectives. UIE not only remarkably improves model performance on the users with sparse interest but also significantly enhance model performance on other users. UIE is an end-to-end solution which is easy to be implemented based on ranking model. Moreover, we expand our solution and apply similar methods to long-tail items, which also achieves excellent improvement. Furthermore, we conduct extensive offline and online experiments in a large-scale industrial RS. The results demonstrate that our model outperforms other models remarkably, especially for the users with sparse interest. Until now, UIE has been fully deployed in multiple large-scale RSs and achieved remarkable improvements. | [
"['Peng Liu' 'Nian Wang' 'Cong Xu' 'Ming Zhao' 'Bin Wang' 'Yi Ren']"
] |
null | null | 2405.13247 | null | null | http://arxiv.org/pdf/2405.13247v1 | 2024-05-21T23:28:20Z | 2024-05-21T23:28:20Z | Improving Earth-like planet detection in radial velocity using deep
learning | Many novel methods have been proposed to mitigate stellar activity for exoplanet detection as the presence of stellar activity in radial velocity (RV) measurements is the current major limitation. Unlike traditional methods that model stellar activity in the RV domain, more methods are moving in the direction of disentangling stellar activity at the spectral level. The goal of this paper is to present a novel convolutional neural network-based algorithm that efficiently models stellar activity signals at the spectral level, enhancing the detection of Earth-like planets. We trained a convolutional neural network to build the correlation between the change in the spectral line profile and the corresponding RV, full width at half maximum (FWHM) and bisector span (BIS) values derived from the classical cross-correlation function. This algorithm has been tested on three intensively observed stars: Alpha Centauri B (HD128621), Tau ceti (HD10700), and the Sun. By injecting simulated planetary signals at the spectral level, we demonstrate that our machine learning algorithm can achieve, for HD128621 and HD10700, a detection threshold of 0.5 m/s in semi-amplitude for planets with periods ranging from 10 to 300 days. This threshold would correspond to the detection of a $sim$4$mathrm{M}_{oplus}$ in the habitable zone of those stars. On the HARPS-N solar dataset, our algorithm is even more efficient at mitigating stellar activity signals and can reach a threshold of 0.2 m/s, which would correspond to a 2.2$mathrm{M}_{oplus}$ planet on the orbit of the Earth. To the best of our knowledge, it is the first time that such low detection thresholds are reported for the Sun, but also for other stars, and therefore this highlights the efficiency of our convolutional neural network-based algorithm at mitigating stellar activity in RV measurements. | [
"['Yinan Zhao' 'Xavier Dumusque' 'Michael Cretignier'\n 'Andrew Collier Cameron' 'David W. Latham' 'Mercedes López-Morales'\n 'Michel Mayor' 'Alessandro Sozzetti' 'Rosario Cosentino'\n 'Isidro Gómez-Vargas' 'Francesco Pepe' 'Stephane Udry']"
] |
null | null | 2405.13254 | null | null | http://arxiv.org/pdf/2405.13254v1 | 2024-05-21T23:48:26Z | 2024-05-21T23:48:26Z | System Safety Monitoring of Learned Components Using Temporal Metric
Forecasting | In learning-enabled autonomous systems, safety monitoring of learned components is crucial to ensure their outputs do not lead to system safety violations, given the operational context of the system. However, developing a safety monitor for practical deployment in real-world applications is challenging. This is due to limited access to internal workings and training data of the learned component. Furthermore, safety monitors should predict safety violations with low latency, while consuming a reasonable amount of computation. To address the challenges, we propose a safety monitoring method based on probabilistic time series forecasting. Given the learned component outputs and an operational context, we empirically investigate different Deep Learning (DL)-based probabilistic forecasting to predict the objective measure capturing the satisfaction or violation of a safety requirement (safety metric). We empirically evaluate safety metric and violation prediction accuracy, and inference latency and resource usage of four state-of-the-art models, with varying horizons, using an autonomous aviation case study. Our results suggest that probabilistic forecasting of safety metrics, given learned component outputs and scenarios, is effective for safety monitoring. Furthermore, for the autonomous aviation case study, Temporal Fusion Transformer (TFT) was the most accurate model for predicting imminent safety violations, with acceptable latency and resource consumption. | [
"['Sepehr Sharifi' 'Andrea Stocco' 'Lionel C. Briand']"
] |
null | null | 2405.13264 | null | null | http://arxiv.org/pdf/2405.13264v1 | 2024-05-22T00:24:17Z | 2024-05-22T00:24:17Z | Part-based Quantitative Analysis for Heatmaps | Heatmaps have been instrumental in helping understand deep network decisions, and are a common approach for Explainable AI (XAI). While significant progress has been made in enhancing the informativeness and accessibility of heatmaps, heatmap analysis is typically very subjective and limited to domain experts. As such, developing automatic, scalable, and numerical analysis methods to make heatmap-based XAI more objective, end-user friendly, and cost-effective is vital. In addition, there is a need for comprehensive evaluation metrics to assess heatmap quality at a granular level. | [
"['Osman Tursun' 'Sinan Kalkan' 'Simon Denman' 'Sridha Sridharan'\n 'Clinton Fookes']"
] |
null | null | 2405.13268 | null | null | http://arxiv.org/pdf/2405.13268v1 | 2024-05-22T00:42:49Z | 2024-05-22T00:42:49Z | Stochastic Online Conformal Prediction with Semi-Bandit Feedback | Conformal prediction has emerged as an effective strategy for uncertainty quantification by modifying a model to output sets of labels instead of a single label. These prediction sets come with the guarantee that they contain the true label with high probability. However, conformal prediction typically requires a large calibration dataset of i.i.d. examples. We consider the online learning setting, where examples arrive over time, and the goal is to construct prediction sets dynamically. Departing from existing work, we assume semi-bandit feedback, where we only observe the true label if it is contained in the prediction set. For instance, consider calibrating a document retrieval model to a new domain; in this setting, a user would only be able to provide the true label if the target document is in the prediction set of retrieved documents. We propose a novel conformal prediction algorithm targeted at this setting, and prove that it obtains sublinear regret compared to the optimal conformal predictor. We evaluate our algorithm on a retrieval task and an image classification task, and demonstrate that it empirically achieves good performance. | [
"['Haosen Ge' 'Hamsa Bastani' 'Osbert Bastani']"
] |
null | null | 2405.13285 | null | null | http://arxiv.org/pdf/2405.13285v2 | 2024-06-25T17:40:35Z | 2024-05-22T01:54:51Z | Enhancing Active Learning for Sentinel 2 Imagery through Contrastive
Learning and Uncertainty Estimation | In this paper, we introduce a novel method designed to enhance label efficiency in satellite imagery analysis by integrating semi-supervised learning (SSL) with active learning strategies. Our approach utilizes contrastive learning together with uncertainty estimations via Monte Carlo Dropout (MC Dropout), with a particular focus on Sentinel-2 imagery analyzed using the Eurosat dataset. We explore the effectiveness of our method in scenarios featuring both balanced and unbalanced class distributions. Our results show that the proposed method performs better than several other popular methods in this field, enabling significant savings in labeling effort while maintaining high classification accuracy. These findings highlight the potential of our approach to facilitate scalable and cost-effective satellite image analysis, particularly advantageous for extensive environmental monitoring and land use classification tasks. | [
"['David Pogorzelski' 'Peter Arlinghaus' 'Wenyan Zhang']"
] |
null | null | 2405.13288 | null | null | http://arxiv.org/pdf/2405.13288v1 | 2024-05-22T02:03:12Z | 2024-05-22T02:03:12Z | Remarks on Loss Function of Threshold Method for Ordinal Regression
Problem | Threshold methods are popular for ordinal regression problems, which are classification problems for data with a natural ordinal relation. They learn a one-dimensional transformation (1DT) of observations of the explanatory variable, and then assign label predictions to the observations by thresholding their 1DT values. In this paper, we study the influence of the underlying data distribution and of the learning procedure of the 1DT on the classification performance of the threshold method via theoretical considerations and numerical experiments. Consequently, for example, we found that threshold methods based on typical learning procedures may perform poorly when the probability distribution of the target variable conditioned on an observation of the explanatory variable tends to be non-unimodal. Another instance of our findings is that learned 1DT values are concentrated at a few points under the learning procedure based on a piecewise-linear loss function, which can make difficult to classify data well. | [
"['Ryoya Yamasaki' 'Toshiyuki Tanaka']"
] |
null | null | 2405.13290 | null | null | http://arxiv.org/pdf/2405.13290v1 | 2024-05-22T02:09:22Z | 2024-05-22T02:09:22Z | Theoretical Analysis of Meta Reinforcement Learning: Generalization
Bounds and Convergence Guarantees | This research delves deeply into Meta Reinforcement Learning (Meta RL) through a exploration focusing on defining generalization limits and ensuring convergence. By employing a approach this article introduces an innovative theoretical framework to meticulously assess the effectiveness and performance of Meta RL algorithms. We present an explanation of generalization limits measuring how well these algorithms can adapt to learning tasks while maintaining consistent results. Our analysis delves into the factors that impact the adaptability of Meta RL revealing the relationship, between algorithm design and task complexity. Additionally we establish convergence assurances by proving conditions under which Meta RL strategies are guaranteed to converge towards solutions. We examine the convergence behaviors of Meta RL algorithms across scenarios providing a comprehensive understanding of the driving forces behind their long term performance. This exploration covers both convergence and real time efficiency offering a perspective, on the capabilities of these algorithms. | [
"['Cangqing Wang' 'Mingxiu Sui' 'Dan Sun' 'Zecheng Zhang' 'Yan Zhou']"
] |
null | null | 2405.13300 | null | null | http://arxiv.org/pdf/2405.13300v3 | 2024-07-01T04:01:11Z | 2024-05-22T02:37:02Z | FAITH: Frequency-domain Attention In Two Horizons for Time Series
Forecasting | Time Series Forecasting plays a crucial role in various fields such as industrial equipment maintenance, meteorology, energy consumption, traffic flow and financial investment. However, despite their considerable advantages over traditional statistical approaches, current deep learning-based predictive models often exhibit a significant deviation between their forecasting outcomes and the ground truth. This discrepancy is largely due to an insufficient emphasis on extracting the sequence's latent information, particularly its global information within the frequency domain and the relationship between different variables. To address this issue, we propose a novel model Frequency-domain Attention In Two Horizons, which decomposes time series into trend and seasonal components using a multi-scale sequence adaptive decomposition and fusion architecture, and processes them separately. FAITH utilizes Frequency Channel feature Extraction Module and Frequency Temporal feature Extraction Module to capture inter-channel relationships and temporal global information in the sequence, significantly improving its ability to handle long-term dependencies and complex patterns. Furthermore, FAITH achieves theoretically linear complexity by modifying the time-frequency domain transformation method, effectively reducing computational costs. Extensive experiments on 6 benchmarks for long-term forecasting and 3 benchmarks for short-term forecasting demonstrate that FAITH outperforms existing models in many fields, such as electricity, weather and traffic, proving its effectiveness and superiority both in long-term and short-term time series forecasting tasks. Our codes and data are available at https://github.com/LRQ577/FAITH. | [
"['Ruiqi Li' 'Maowei Jiang' 'Kai Wang' 'Kaiduo Feng' 'Quangao Liu'\n 'Yue Sun' 'Xiufang Zhou']"
] |
null | null | 2405.13302 | null | null | http://arxiv.org/pdf/2405.13302v1 | 2024-05-22T02:44:46Z | 2024-05-22T02:44:46Z | Accelerated Evaluation of Ollivier-Ricci Curvature Lower Bounds:
Bridging Theory and Computation | Curvature serves as a potent and descriptive invariant, with its efficacy validated both theoretically and practically within graph theory. We employ a definition of generalized Ricci curvature proposed by Ollivier, which Lin and Yau later adapted to graph theory, known as Ollivier-Ricci curvature (ORC). ORC measures curvature using the Wasserstein distance, thereby integrating geometric concepts with probability theory and optimal transport. Jost and Liu previously discussed the lower bound of ORC by showing the upper bound of the Wasserstein distance. We extend the applicability of these bounds to discrete spaces with metrics on integers, specifically hypergraphs. Compared to prior work on ORC in hypergraphs by Coupette, Dalleiger, and Rieck, which faced computational challenges, our method introduces a simplified approach with linear computational complexity, making it particularly suitable for analyzing large-scale networks. Through extensive simulations and application to synthetic and real-world datasets, we demonstrate the significant improvements our method offers in evaluating ORC. | [
"['Wonwoo Kang' 'Heehyun Park']"
] |
null | null | 2405.13304 | null | null | http://arxiv.org/pdf/2405.13304v1 | 2024-05-22T02:46:26Z | 2024-05-22T02:46:26Z | Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation | Brain tumor segmentation is a critical task in medical image analysis, aiding in the diagnosis and treatment planning of brain tumor patients. The importance of automated and accurate brain tumor segmentation cannot be overstated. It enables medical professionals to precisely delineate tumor regions, assess tumor growth or regression, and plan targeted treatments. Various deep learning-based techniques proposed in the literature have made significant progress in this field, however, they still face limitations in terms of accuracy due to the complex and variable nature of brain tumor morphology. In this research paper, we propose a novel Hybrid Multihead Attentive U-Net architecture, to address the challenges in accurate brain tumor segmentation, and to capture complex spatial relationships and subtle tumor boundaries. The U-Net architecture has proven effective in capturing contextual information and feature representations, while attention mechanisms enhance the model's ability to focus on informative regions and refine the segmentation boundaries. By integrating these two components, our proposed architecture improves accuracy in brain tumor segmentation. We test our proposed model on the BraTS 2020 benchmark dataset and compare its performance with the state-of-the-art well-known SegNet, FCN-8s, and Dense121 U-Net architectures. The results show that our proposed model outperforms the others in terms of the evaluated performance metrics. | [
"['Muhammad Ansab Butt' 'Absaar Ul Jabbar']"
] |
null | null | 2405.13319 | null | null | http://arxiv.org/pdf/2405.13319v1 | 2024-05-22T03:25:35Z | 2024-05-22T03:25:35Z | ''You should probably read this'': Hedge Detection in Text | Humans express ideas, beliefs, and statements through language. The manner of expression can carry information indicating the author's degree of confidence in their statement. Understanding the certainty level of a claim is crucial in areas such as medicine, finance, engineering, and many others where errors can lead to disastrous results. In this work, we apply a joint model that leverages words and part-of-speech tags to improve hedge detection in text and achieve a new top score on the CoNLL-2010 Wikipedia corpus. | [
"['Denys Katerenchuk' 'Rivka Levitan']"
] |
null | null | 2405.13324 | null | null | http://arxiv.org/pdf/2405.13324v1 | 2024-05-22T03:47:55Z | 2024-05-22T03:47:55Z | Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble
of Teachers | Adversarial training (AT) is a popular method for training robust deep neural networks (DNNs) against adversarial attacks. Yet, AT suffers from two shortcomings: (i) the robustness of DNNs trained by AT is highly intertwined with the size of the DNNs, posing challenges in achieving robustness in smaller models; and (ii) the adversarial samples employed during the AT process exhibit poor generalization, leaving DNNs vulnerable to unforeseen attack types. To address these dual challenges, this paper introduces adversarial training via adaptive knowledge amalgamation of an ensemble of teachers (AT-AKA). In particular, we generate a diverse set of adversarial samples as the inputs to an ensemble of teachers; and then, we adaptively amalgamate the logtis of these teachers to train a generalized-robust student. Through comprehensive experiments, we illustrate the superior efficacy of AT-AKA over existing AT methods and adversarial robustness distillation techniques against cutting-edge attacks, including AutoAttack. | [
"['Shayan Mohajer Hamidi' 'Linfeng Ye']"
] |
null | null | 2405.13345 | null | null | http://arxiv.org/pdf/2405.13345v1 | 2024-05-22T05:04:44Z | 2024-05-22T05:04:44Z | Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human
Intervention | Reinforcement learning (RL) provides a compelling framework for enabling autonomous vehicles to continue to learn and improve diverse driving behaviors on their own. However, training real-world autonomous vehicles with current RL algorithms presents several challenges. One critical challenge, often overlooked in these algorithms, is the need to reset a driving environment between every episode. While resetting an environment after each episode is trivial in simulated settings, it demands significant human intervention in the real world. In this paper, we introduce a novel autonomous algorithm that allows off-the-shelf RL algorithms to train an autonomous vehicle with minimal human intervention. Our algorithm takes into account the learning progress of the autonomous vehicle to determine when to abort episodes before it enters unsafe states and where to reset it for subsequent episodes in order to gather informative transitions. The learning progress is estimated based on the novelty of both current and future states. We also take advantage of rule-based autonomous driving algorithms to safely reset an autonomous vehicle to an initial state. We evaluate our algorithm against baselines on diverse urban driving tasks. The experimental results show that our algorithm is task-agnostic and achieves better driving performance with fewer manual resets than baselines. | [
"['Sang-Hyun Lee' 'Daehyeok Kwon' 'Seung-Woo Seo']"
] |
null | null | 2405.13347 | null | null | http://arxiv.org/pdf/2405.13347v1 | 2024-05-22T05:07:56Z | 2024-05-22T05:07:56Z | Time-Series Forecasting and Sequence Learning Using Memristor-based
Reservoir System | Pushing the frontiers of time-series information processing in ever-growing edge devices with stringent resources has been impeded by the system's ability to process information and learn locally on the device. Local processing and learning typically demand intensive computations and massive storage as the process involves retrieving information and tuning hundreds of parameters back in time. In this work, we developed a memristor-based echo state network accelerator that features efficient temporal data processing and in-situ online learning. The proposed design is benchmarked using various datasets involving real-world tasks, such as forecasting the load energy consumption and weather conditions. The experimental results illustrate that the hardware model experiences a marginal degradation (~4.8%) in performance as compared to the software model. This is mainly attributed to the limited precision and dynamic range of network parameters when emulated using memristor devices. The proposed system is evaluated for lifespan, robustness, and energy-delay product. It is observed that the system demonstrates a reasonable robustness for device failure below 10%, which may occur due to stuck-at faults. Furthermore, 246X reduction in energy consumption is achieved when compared to a custom CMOS digital design implemented at the same technology node. | [
"['Abdullah M. Zyarah' 'Dhireesha Kudithipudi']"
] |
null | null | 2405.13348 | null | null | http://arxiv.org/pdf/2405.13348v1 | 2024-05-22T05:10:13Z | 2024-05-22T05:10:13Z | On the Challenges of Creating Datasets for Analyzing Commercial Sex
Advertisements to Assess Human Trafficking Risk and Organized Activity | Our study addresses the challenges of building datasets to understand the risks associated with organized activities and human trafficking through commercial sex advertisements. These challenges include data scarcity, rapid obsolescence, and privacy concerns. Traditional approaches, which are not automated and are difficult to reproduce, fall short in addressing these issues. We have developed a reproducible and automated methodology to analyze five million advertisements. In the process, we identified further challenges in dataset creation within this sensitive domain. This paper presents a streamlined methodology to assist researchers in constructing effective datasets for combating organized crime, allowing them to focus on advancing detection technologies. | [
"['Pablo Rivas' 'Tomas Cerny' 'Alejandro Rodriguez Perez' 'Javier Turek'\n 'Laurie Giddens' 'Gisela Bichler' 'Stacie Petter']"
] |
null | null | 2405.13350 | null | null | http://arxiv.org/pdf/2405.13350v2 | 2024-05-30T18:42:45Z | 2024-05-22T05:12:35Z | Efficacy of ByT5 in Multilingual Translation of Biblical Texts for
Underrepresented Languages | This study presents the development and evaluation of a ByT5-based multilingual translation model tailored for translating the Bible into underrepresented languages. Utilizing the comprehensive Johns Hopkins University Bible Corpus, we trained the model to capture the intricate nuances of character-based and morphologically rich languages. Our results, measured by the BLEU score and supplemented with sample translations, suggest the model can improve accessibility to sacred texts. It effectively handles the distinctive biblical lexicon and structure, thus bridging the linguistic divide. The study also discusses the model's limitations and suggests pathways for future enhancements, focusing on expanding access to sacred literature across linguistic boundaries. | [
"['Corinne Aars' 'Lauren Adams' 'Xiaokan Tian' 'Zhaoyu Wang'\n 'Colton Wismer' 'Jason Wu' 'Pablo Rivas' 'Korn Sooksatra' 'Matthew Fendt']"
] |
null | null | 2405.13360 | null | null | http://arxiv.org/pdf/2405.13360v1 | 2024-05-22T05:33:47Z | 2024-05-22T05:33:47Z | How to Trace Latent Generative Model Generated Images without Artificial
Watermark? | Latent generative models (e.g., Stable Diffusion) have become more and more popular, but concerns have arisen regarding potential misuse related to images generated by these models. It is, therefore, necessary to analyze the origin of images by inferring if a particular image was generated by a specific latent generative model. Most existing methods (e.g., image watermark and model fingerprinting) require extra steps during training or generation. These requirements restrict their usage on the generated images without such extra operations, and the extra required operations might compromise the quality of the generated images. In this work, we ask whether it is possible to effectively and efficiently trace the images generated by a specific latent generative model without the aforementioned requirements. To study this problem, we design a latent inversion based method called LatentTracer to trace the generated images of the inspected model by checking if the examined images can be well-reconstructed with an inverted latent input. We leverage gradient based latent inversion and identify a encoder-based initialization critical to the success of our approach. Our experiments on the state-of-the-art latent generative models, such as Stable Diffusion, show that our method can distinguish the images generated by the inspected model and other images with a high accuracy and efficiency. Our findings suggest the intriguing possibility that today's latent generative generated images are naturally watermarked by the decoder used in the source models. Code: https://github.com/ZhentingWang/LatentTracer. | [
"['Zhenting Wang' 'Vikash Sehwag' 'Chen Chen' 'Lingjuan Lyu'\n 'Dimitris N. Metaxas' 'Shiqing Ma']"
] |
null | null | 2405.13362 | null | null | http://arxiv.org/pdf/2405.13362v1 | 2024-05-22T05:43:15Z | 2024-05-22T05:43:15Z | Lusifer: LLM-based User SImulated Feedback Environment for online
Recommender systems | Training reinforcement learning-based recommender systems are often hindered by the lack of dynamic and realistic user interactions. Lusifer, a novel environment leveraging Large Language Models (LLMs), addresses this limitation by generating simulated user feedback. It synthesizes user profiles and interaction histories to simulate responses and behaviors toward recommended items. In addition, user profiles are updated after each rating to reflect evolving user characteristics. Using the MovieLens100K dataset as proof of concept, Lusifer demonstrates accurate emulation of user behavior and preferences. This paper presents Lusifer's operational pipeline, including prompt generation and iterative user profile updates. While validating Lusifer's ability to produce realistic dynamic feedback, future research could utilize this environment to train reinforcement learning systems, offering a scalable and adjustable framework for user simulation in online recommender systems. | [
"['Danial Ebrat' 'Luis Rueda']"
] |
null | null | 2405.13365 | null | null | http://arxiv.org/pdf/2405.13365v1 | 2024-05-22T05:48:25Z | 2024-05-22T05:48:25Z | Clipped Uniform Quantizers for Communication-Efficient Federated
Learning | This paper introduces an approach to employ clipped uniform quantization in federated learning settings, aiming to enhance model efficiency by reducing communication overhead without compromising accuracy. By employing optimal clipping thresholds and adaptive quantization schemes, our method significantly curtails the bit requirements for model weight transmissions between clients and the server. We explore the implications of symmetric clipping and uniform quantization on model performance, highlighting the utility of stochastic quantization to mitigate quantization artifacts and improve model robustness. Through extensive simulations on the MNIST dataset, our results demonstrate that the proposed method achieves near full-precision performance while ensuring substantial communication savings. Specifically, our approach facilitates efficient weight averaging based on quantization errors, effectively balancing the trade-off between communication efficiency and model accuracy. The comparative analysis with conventional quantization methods further confirms the superiority of our technique. | [
"['Zavareh Bozorgasl' 'Hao Chen']"
] |
null | null | 2405.13370 | null | null | http://arxiv.org/pdf/2405.13370v1 | 2024-05-22T06:10:54Z | 2024-05-22T06:10:54Z | Low-Resolution Chest X-ray Classification via Knowledge Distillation and
Multi-task Learning | This research addresses the challenges of diagnosing chest X-rays (CXRs) at low resolutions, a common limitation in resource-constrained healthcare settings. High-resolution CXR imaging is crucial for identifying small but critical anomalies, such as nodules or opacities. However, when images are downsized for processing in Computer-Aided Diagnosis (CAD) systems, vital spatial details and receptive fields are lost, hampering diagnosis accuracy. To address this, this paper presents the Multilevel Collaborative Attention Knowledge (MLCAK) method. This approach leverages the self-attention mechanism of Vision Transformers (ViT) to transfer critical diagnostic knowledge from high-resolution images to enhance the diagnostic efficacy of low-resolution CXRs. MLCAK incorporates local pathological findings to boost model explainability, enabling more accurate global predictions in a multi-task framework tailored for low-resolution CXR analysis. Our research, utilizing the Vindr CXR dataset, shows a considerable enhancement in the ability to diagnose diseases from low-resolution images (e.g. 28 x 28), suggesting a critical transition from the traditional reliance on high-resolution imaging (e.g. 224 x 224). | [
"['Yasmeena Akhter' 'Rishabh Ranjan' 'Richa Singh' 'Mayank Vatsa']"
] |
null | null | 2405.13372 | null | null | http://arxiv.org/pdf/2405.13372v3 | 2024-06-14T08:01:09Z | 2024-05-22T06:15:50Z | Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks | Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intricate associations in data, though scalability is a notable challenge due to memory limitations. In this study, we introduce a new adaptive sampling strategy specifically designed for hypergraphs, which tackles their unique complexities in an efficient manner. We also present a Random Hyperedge Augmentation (RHA) technique and an additional Multilayer Perceptron (MLP) module to improve the robustness and generalization capabilities of our approach. Thorough experiments with real-world datasets have proven the effectiveness of our method, markedly reducing computational and memory demands while maintaining performance levels akin to conventional HGNNs and other baseline models. This research paves the way for improving both the scalability and efficacy of HGNNs in extensive applications. We will also make our codebase publicly accessible. | [
"['Shuai Wang' 'David W. Zhang' 'Jia-Hong Huang' 'Stevan Rudinac'\n 'Monika Kackovic' 'Nachoem Wijnberg' 'Marcel Worring']"
] |
null | null | 2405.13375 | null | null | http://arxiv.org/pdf/2405.13375v1 | 2024-05-22T06:17:58Z | 2024-05-22T06:17:58Z | Adaptive Data Analysis for Growing Data | Reuse of data in adaptive workflows poses challenges regarding overfitting and the statistical validity of results. Previous work has demonstrated that interacting with data via differentially private algorithms can mitigate overfitting, achieving worst-case generalization guarantees with asymptotically optimal data requirements. However, such past work assumes data is static and cannot accommodate situations where data grows over time. In this paper we address this gap, presenting the first generalization bounds for adaptive analysis in the dynamic data setting. We allow the analyst to adaptively schedule their queries conditioned on the current size of the data, in addition to previous queries and responses. We also incorporate time-varying empirical accuracy bounds and mechanisms, allowing for tighter guarantees as data accumulates. In a batched query setting, the asymptotic data requirements of our bound grows with the square-root of the number of adaptive queries, matching prior works' improvement over data splitting for the static setting. We instantiate our bound for statistical queries with the clipped Gaussian mechanism, where it empirically outperforms baselines composed from static bounds. | [
"['Neil G. Marchant' 'Benjamin I. P. Rubinstein']"
] |
null | null | 2405.13378 | null | null | http://arxiv.org/pdf/2405.13378v1 | 2024-05-22T06:19:43Z | 2024-05-22T06:19:43Z | FedCache 2.0: Exploiting the Potential of Distilled Data in Knowledge
Cache-driven Federated Learning | Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model training with limited and uncertain communication. In this paper, we introduce FedCache 2.0, a novel personalized FEL architecture that simultaneously addresses these challenges. FedCache 2.0 incorporates the benefits of both dataset distillation and knowledge cache-driven federated learning by storing and organizing distilled data as knowledge in the server-side knowledge cache. Moreover, a device-centric cache sampling strategy is introduced to tailor transferred knowledge for individual devices within controlled communication bandwidth. Extensive experiments on five datasets covering image recognition, audio understanding, and mobile sensor data mining tasks demonstrate that (1) FedCache 2.0 significantly outperforms state-of-the-art methods regardless of model structures, data distributions, and modalities. (2) FedCache 2.0 can train splendid personalized on-device models with at least $times$28.6 improvement in communication efficiency. | [
"['Quyang Pan' 'Sheng Sun' 'Zhiyuan Wu' 'Yuwei Wang' 'Min Liu' 'Bo Gao']"
] |
null | null | 2405.13381 | null | null | http://arxiv.org/pdf/2405.13381v2 | 2024-05-29T05:25:49Z | 2024-05-22T06:30:55Z | Optimizing Search Advertising Strategies: Integrating Reinforcement
Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and
Bidding | This paper explores the integration of strategic optimization methods in search advertising, focusing on ad ranking and bidding mechanisms within E-commerce platforms. By employing a combination of reinforcement learning and evolutionary strategies, we propose a dynamic model that adjusts to varying user interactions and optimizes the balance between advertiser cost, user relevance, and platform revenue. Our results suggest significant improvements in ad placement accuracy and cost efficiency, demonstrating the model's applicability in real-world scenarios. | [
"['Chang Zhou' 'Yang Zhao' 'Jin Cao' 'Yi Shen' 'Xiaoling Cui' 'Chiyu Cheng']"
] |
null | null | 2405.13383 | null | null | http://arxiv.org/pdf/2405.13383v2 | 2024-07-03T05:27:45Z | 2024-05-22T06:33:48Z | Gradient Projection For Continual Parameter-Efficient Tuning | Parameter-efficient tunings (PETs) have demonstrated impressive performance and promising perspectives in training large models, while they are still confronted with a common problem: the trade-off between learning new content and protecting old knowledge, e.g., zero-shot generalization ability, and cross-modal hallucination. In this paper, we reformulate Adapter, LoRA, Prefix-tuning, and Prompt-tuning from the perspective of gradient projection, and firstly propose a unified framework called Parameter Efficient Gradient Projection (PEGP). We introduce orthogonal gradient projection into different PET paradigms and theoretically demonstrate that the orthogonal condition for the gradient can effectively resist forgetting even for large-scale models. It therefore modifies the gradient towards the direction that has less impact on the old feature space, with less extra memory space and training time. We extensively evaluate our method with different backbones, including ViT and CLIP, on diverse datasets, and experiments comprehensively demonstrate its efficiency in reducing forgetting in class, online class, domain, task, and multi-modality continual settings. The project page is available at https://dmcv-ecnu-pegp.github.io/. | [
"['Jingyang Qiao' 'Zhizhong Zhang' 'Xin Tan' 'Yanyun Qu' 'Wensheng Zhang'\n 'Zhi Han' 'Yuan Xie']"
] |
null | null | 2405.13390 | null | null | http://arxiv.org/pdf/2405.13390v3 | 2024-06-28T21:45:11Z | 2024-05-22T07:02:35Z | Convergence analysis of kernel learning FBSDE filter | Kernel learning forward backward SDE filter is an iterative and adaptive meshfree approach to solve the nonlinear filtering problem. It builds from forward backward SDE for Fokker-Planker equation, which defines evolving density for the state variable, and employs KDE to approximate density. This algorithm has shown more superior performance than mainstream particle filter method, in both convergence speed and efficiency of solving high dimension problems. However, this method has only been shown to converge empirically. In this paper, we present a rigorous analysis to demonstrate its local and global convergence, and provide theoretical support for its empirical results. | [
"['Yunzheng Lyu' 'Feng Bao']"
] |
null | null | 2405.13392 | null | null | http://arxiv.org/pdf/2405.13392v1 | 2024-05-22T07:07:22Z | 2024-05-22T07:07:22Z | Local convergence of min-max algorithms to differentiable equilibrium on
Riemannian manifold | We study min-max algorithms to solve zero-sum differentiable games on Riemannian manifold. The notions of differentiable Stackelberg equilibrium and differentiable Nash equilibrium in Euclidean space are generalized to Riemannian manifold, through an intrinsic definition which does not depend on the choice of local coordinate chart of manifold. We then provide sufficient conditions for the local convergence of the deterministic simultaneous algorithms $tau$-GDA and $tau$-SGA near such equilibrium, using a general methodology based on spectral analysis. These algorithms are extended with stochastic gradients and applied to the training of Wasserstein GAN. The discriminator of GAN is constructed from Lipschitz-continuous functions based on Stiefel manifold. We show numerically how the insights obtained from the local convergence analysis may lead to an improvement of GAN models. | [
"['Sixin Zhang']"
] |
null | null | 2405.13393 | null | null | http://arxiv.org/pdf/2405.13393v1 | 2024-05-22T07:08:27Z | 2024-05-22T07:08:27Z | NFCL: Simply interpretable neural networks for a short-term multivariate
forecasting | Multivariate time-series forecasting (MTSF) stands as a compelling field within the machine learning community. Diverse neural network based methodologies deployed in MTSF applications have demonstrated commendable efficacy. Despite the advancements in model performance, comprehending the rationale behind the model's behavior remains an enigma. Our proposed model, the Neural ForeCasting Layer (NFCL), employs a straightforward amalgamation of neural networks. This uncomplicated integration ensures that each neural network contributes inputs and predictions independently, devoid of interference from other inputs. Consequently, our model facilitates a transparent explication of forecast results. This paper introduces NFCL along with its diverse extensions. Empirical findings underscore NFCL's superior performance compared to nine benchmark models across 15 available open datasets. Notably, NFCL not only surpasses competitors but also provides elucidation for its predictions. In addition, Rigorous experimentation involving diverse model structures bolsters the justification of NFCL's unique configuration. | [
"['Wonkeun Jo' 'Dongil Kim']"
] |
null | null | 2405.13396 | null | null | http://arxiv.org/pdf/2405.13396v1 | 2024-05-22T07:13:55Z | 2024-05-22T07:13:55Z | Why In-Context Learning Transformers are Tabular Data Classifiers | The recently introduced TabPFN pretrains an In-Context Learning (ICL) transformer on synthetic data to perform tabular data classification. As synthetic data does not share features or labels with real-world data, the underlying mechanism that contributes to the success of this method remains unclear. This study provides an explanation by demonstrating that ICL-transformers acquire the ability to create complex decision boundaries during pretraining. To validate our claim, we develop a novel forest dataset generator which creates datasets that are unrealistic, but have complex decision boundaries. Our experiments confirm the effectiveness of ICL-transformers pretrained on this data. Furthermore, we create TabForestPFN, the ICL-transformer pretrained on both the original TabPFN synthetic dataset generator and our forest dataset generator. By fine-tuning this model, we reach the current state-of-the-art on tabular data classification. Code is available at https://github.com/FelixdenBreejen/TabForestPFN. | [
"['Felix den Breejen' 'Sangmin Bae' 'Stephen Cha' 'Se-Young Yun']"
] |
null | null | 2405.13407 | null | null | http://arxiv.org/pdf/2405.13407v1 | 2024-05-22T07:33:24Z | 2024-05-22T07:33:24Z | Dynamic Context Adaptation and Information Flow Control in Transformers:
Introducing the Evaluator Adjuster Unit and Gated Residual Connections | Transformers have revolutionized various domains of artificial intelligence due to their unique ability to model long-range dependencies in data. However, they lack in nuanced, context-dependent modulation of features and information flow. This paper introduces two significant enhancements to the transformer architecture - the Evaluator Adjuster Unit (EAU) and Gated Residual Connections (GRC) - designed to address these limitations. The EAU dynamically modulates attention outputs based on the relevance of the input context, allowing for more adaptive response patterns. Concurrently, the GRC modifies the transformer's residual connections through a gating mechanism that selectively controls the information flow, thereby enhancing the network's ability to focus on contextually important features. We evaluate the performance of these enhancements across several benchmarks in natural language processing. Our results demonstrate improved adaptability and efficiency, suggesting that these modifications could set new standards for designing flexible and context-aware transformer models. | [
"['Sahil Rajesh Dhayalkar']"
] |
null | null | 2405.13413 | null | null | http://arxiv.org/pdf/2405.13413v1 | 2024-05-22T07:48:24Z | 2024-05-22T07:48:24Z | Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for
6G Networks | Ensuring extremely high reliability is essential for channel coding in 6G networks. The next-generation of ultra-reliable and low-latency communications (xURLLC) scenario within 6G networks requires a frame error rate (FER) below 10-9. However, low-density parity-check (LDPC) codes, the standard in 5G new radio (NR), encounter a challenge known as the error floor phenomenon, which hinders to achieve such low rates. To tackle this problem, we introduce an innovative solution: boosted neural min-sum (NMS) decoder. This decoder operates identically to conventional NMS decoders, but is trained by novel training methods including: i) boosting learning with uncorrected vectors, ii) block-wise training schedule to address the vanishing gradient issue, iii) dynamic weight sharing to minimize the number of trainable weights, iv) transfer learning to reduce the required sample count, and v) data augmentation to expedite the sampling process. Leveraging these training strategies, the boosted NMS decoder achieves the state-of-the art performance in reducing the error floor as well as superior waterfall performance. Remarkably, we fulfill the 6G xURLLC requirement for 5G LDPC codes without the severe error floor. Additionally, the boosted NMS decoder, once its weights are trained, can perform decoding without additional modules, making it highly practical for immediate application. | [
"['Hee-Youl Kwak' 'Dae-Young Yun' 'Yongjune Kim' 'Sang-Hyo Kim'\n 'Jong-Seon No']"
] |
null | null | 2405.13427 | null | null | http://arxiv.org/pdf/2405.13427v1 | 2024-05-22T08:15:50Z | 2024-05-22T08:15:50Z | Adaptive Fuzzy C-Means with Graph Embedding | Fuzzy clustering algorithms can be roughly categorized into two main groups: Fuzzy C-Means (FCM) based methods and mixture model based methods. However, for almost all existing FCM based methods, how to automatically selecting proper membership degree hyper-parameter values remains a challenging and unsolved problem. Mixture model based methods, while circumventing the difficulty of manually adjusting membership degree hyper-parameters inherent in FCM based methods, often have a preference for specific distributions, such as the Gaussian distribution. In this paper, we propose a novel FCM based clustering model that is capable of automatically learning an appropriate membership degree hyper-parameter value and handling data with non-Gaussian clusters. Moreover, by removing the graph embedding regularization, the proposed FCM model can degenerate into the simplified generalized Gaussian mixture model. Therefore, the proposed FCM model can be also seen as the generalized Gaussian mixture model with graph embedding. Extensive experiments are conducted on both synthetic and real-world datasets to demonstrate the effectiveness of the proposed model. | [
"['Qiang Chen' 'Weizhong Yu' 'Feiping Nie' 'Xuelong Li']"
] |
null | null | 2405.13445 | null | null | http://arxiv.org/pdf/2405.13445v1 | 2024-05-22T08:37:37Z | 2024-05-22T08:37:37Z | Task-agnostic Decision Transformer for Multi-type Agent Control with
Federated Split Training | With the rapid advancements in artificial intelligence, the development of knowledgeable and personalized agents has become increasingly prevalent. However, the inherent variability in state variables and action spaces among personalized agents poses significant aggregation challenges for traditional federated learning algorithms. To tackle these challenges, we introduce the Federated Split Decision Transformer (FSDT), an innovative framework designed explicitly for AI agent decision tasks. The FSDT framework excels at navigating the intricacies of personalized agents by harnessing distributed data for training while preserving data privacy. It employs a two-stage training process, with local embedding and prediction models on client agents and a global transformer decoder model on the server. Our comprehensive evaluation using the benchmark D4RL dataset highlights the superior performance of our algorithm in federated split learning for personalized agents, coupled with significant reductions in communication and computational overhead compared to traditional centralized training approaches. The FSDT framework demonstrates strong potential for enabling efficient and privacy-preserving collaborative learning in applications such as autonomous driving decision systems. Our findings underscore the efficacy of the FSDT framework in effectively leveraging distributed offline reinforcement learning data to enable powerful multi-type agent decision systems. | [
"['Zhiyuan Wang' 'Bokui Chen' 'Xiaoyang Qu' 'Zhenhou Hong' 'Jing Xiao'\n 'Jianzong Wang']"
] |
null | null | 2405.13449 | null | null | http://arxiv.org/pdf/2405.13449v1 | 2024-05-22T08:41:32Z | 2024-05-22T08:41:32Z | Input Guided Multiple Deconstruction Single Reconstruction neural
network models for Matrix Factorization | Referring back to the original text in the course of hierarchical learning is a common human trait that ensures the right direction of learning. The models developed based on the concept of Non-negative Matrix Factorization (NMF), in this paper are inspired by this idea. They aim to deal with high-dimensional data by discovering its low rank approximation by determining a unique pair of factor matrices. The model, named Input Guided Multiple Deconstruction Single Reconstruction neural network for Non-negative Matrix Factorization (IG-MDSR-NMF), ensures the non-negativity constraints of both factors. Whereas Input Guided Multiple Deconstruction Single Reconstruction neural network for Relaxed Non-negative Matrix Factorization (IG-MDSR-RNMF) introduces a novel idea of factorization with only the basis matrix adhering to the non-negativity criteria. This relaxed version helps the model to learn more enriched low dimensional embedding of the original data matrix. The competency of preserving the local structure of data in its low rank embedding produced by both the models has been appropriately verified. The superiority of low dimensional embedding over that of the original data justifying the need for dimension reduction has been established. The primacy of both the models has also been validated by comparing their performances separately with that of nine other established dimension reduction algorithms on five popular datasets. Moreover, computational complexity of the models and convergence analysis have also been presented testifying to the supremacy of the models. | [
"['Prasun Dutta' 'Rajat K. De']"
] |
null | null | 2405.13453 | null | null | http://arxiv.org/pdf/2405.13453v1 | 2024-05-22T08:46:45Z | 2024-05-22T08:46:45Z | A Huber Loss Minimization Approach to Mean Estimation under User-level
Differential Privacy | Privacy protection of users' entire contribution of samples is important in distributed systems. The most effective approach is the two-stage scheme, which finds a small interval first and then gets a refined estimate by clipping samples into the interval. However, the clipping operation induces bias, which is serious if the sample distribution is heavy-tailed. Besides, users with large local sample sizes can make the sensitivity much larger, thus the method is not suitable for imbalanced users. Motivated by these challenges, we propose a Huber loss minimization approach to mean estimation under user-level differential privacy. The connecting points of Huber loss can be adaptively adjusted to deal with imbalanced users. Moreover, it avoids the clipping operation, thus significantly reducing the bias compared with the two-stage approach. We provide a theoretical analysis of our approach, which gives the noise strength needed for privacy protection, as well as the bound of mean squared error. The result shows that the new method is much less sensitive to the imbalance of user-wise sample sizes and the tail of sample distributions. Finally, we perform numerical experiments to validate our theoretical analysis. | [
"['Puning Zhao' 'Lifeng Lai' 'Li Shen' 'Qingming Li' 'Jiafei Wu' 'Zhe Liu']"
] |
null | null | 2405.13456 | null | null | http://arxiv.org/pdf/2405.13456v1 | 2024-05-22T08:58:51Z | 2024-05-22T08:58:51Z | Deep linear networks for regression are implicitly regularized towards
flat minima | The largest eigenvalue of the Hessian, or sharpness, of neural networks is a key quantity to understand their optimization dynamics. In this paper, we study the sharpness of deep linear networks for overdetermined univariate regression. Minimizers can have arbitrarily large sharpness, but not an arbitrarily small one. Indeed, we show a lower bound on the sharpness of minimizers, which grows linearly with depth. We then study the properties of the minimizer found by gradient flow, which is the limit of gradient descent with vanishing learning rate. We show an implicit regularization towards flat minima: the sharpness of the minimizer is no more than a constant times the lower bound. The constant depends on the condition number of the data covariance matrix, but not on width or depth. This result is proven both for a small-scale initialization and a residual initialization. Results of independent interest are shown in both cases. For small-scale initialization, we show that the learned weight matrices are approximately rank-one and that their singular vectors align. For residual initialization, convergence of the gradient flow for a Gaussian initialization of the residual network is proven. Numerical experiments illustrate our results and connect them to gradient descent with non-vanishing learning rate. | [
"['Pierre Marion' 'Lénaïc Chizat']"
] |
null | null | 2405.13468 | null | null | http://arxiv.org/pdf/2405.13468v1 | 2024-05-22T09:25:58Z | 2024-05-22T09:25:58Z | Machine learning for exoplanet detection in high-contrast spectroscopy
Combining cross correlation maps and deep learning on medium-resolution
integral-field spectra | The advent of high-contrast imaging instruments combined with medium-resolution spectrographs allows spectral and temporal dimensions to be combined with spatial dimensions to detect and potentially characterize exoplanets with higher sensitivity. We develop a new method to effectively leverage the spectral and spatial dimensions in integral-field spectroscopy (IFS) datasets using a supervised deep-learning algorithm to improve the detection sensitivity to high-contrast exoplanets. We begin by applying a data transform whereby the IFS datasets are replaced by cross-correlation coefficient tensors obtained by cross-correlating our data with young gas giant spectral template spectra. This transformed data is then used to train machine learning (ML) algorithms. We train a 2D CNN and 3D LSTM with our data. We compare the ML models with a non-ML algorithm, based on the STIM map of arXiv:1810.06895. We test our algorithms on simulated young gas giants in a dataset that contains no known exoplanet, and explore the sensitivity of algorithms to detect these exoplanets at contrasts ranging from 1e-3 to 1e-4 at different radial separations. We quantify the sensitivity using modified receiver operating characteristic curves (mROC). We discover that the ML algorithms produce fewer false positives and have a higher true positive rate than the STIM-based algorithm, and the true positive rate of ML algorithms is less impacted by changing radial separation. We discover that the velocity dimension is an important differentiating factor. Through this paper, we demonstrate that ML techniques have the potential to improve the detection limits and reduce false positives for directly imaged planets in IFS datasets, after transforming the spectral dimension into a radial velocity dimension through a cross-correlation operation. | [
"['Rakesh Nath-Ranga' 'Olivier Absil' 'Valentin Christiaens'\n 'Emily O. Garvin']"
] |
null | null | 2405.13469 | null | null | http://arxiv.org/pdf/2405.13469v1 | 2024-05-22T09:25:58Z | 2024-05-22T09:25:58Z | Machine Learning for Exoplanet Detection in High-Contrast Spectroscopy:
Revealing Exoplanets by Leveraging Hidden Molecular Signatures in
Cross-Correlated Spectra with Convolutional Neural Networks | The new generation of observatories and instruments (VLT/ERIS, JWST, ELT) motivate the development of robust methods to detect and characterise faint and close-in exoplanets. Molecular mapping and cross-correlation for spectroscopy use molecular templates to isolate a planet's spectrum from its host star. However, reliance on signal-to-noise ratio (S/N) metrics can lead to missed discoveries, due to strong assumptions of Gaussian independent and identically distributed noise. We introduce machine learning for cross-correlation spectroscopy (MLCCS); the method aims to leverage weak assumptions on exoplanet characterisation, such as the presence of specific molecules in atmospheres, to improve detection sensitivity for exoplanets. MLCCS methods, including a perceptron and unidimensional convolutional neural networks, operate in the cross-correlated spectral dimension, in which patterns from molecules can be identified. We test on mock datasets of synthetic planets inserted into real noise from SINFONI at K-band. The results from MLCCS show outstanding improvements. The outcome on a grid of faint synthetic gas giants shows that for a false discovery rate up to 5%, a perceptron can detect about 26 times the amount of planets compared to an S/N metric. This factor increases up to 77 times with convolutional neural networks, with a statistical sensitivity shift from 0.7% to 55.5%. In addition, MLCCS methods show a drastic improvement in detection confidence and conspicuity on imaging spectroscopy. Once trained, MLCCS methods offer sensitive and rapid detection of exoplanets and their molecular species in the spectral dimension. They handle systematic noise and challenging seeing conditions, can adapt to many spectroscopic instruments and modes, and are versatile regarding atmospheric characteristics, which can enable identification of various planets in archival and future data. | [
"['Emily O. Garvin' 'Markus J. Bonse' 'Jean Hayoz' 'Gabriele Cugno'\n 'Jonas Spiller' 'Polychronis A. Patapis'\n 'Dominique Petit Dit de la Roche' 'Rakesh Nath-Ranga' 'Olivier Absil'\n 'Nicolai F. Meinshausen' 'Sascha P. Quanz']"
] |
null | null | 2405.13474 | null | null | http://arxiv.org/pdf/2405.13474v1 | 2024-05-22T09:32:24Z | 2024-05-22T09:32:24Z | Why do explanations fail? A typology and discussion on failures in XAI | As Machine Learning (ML) models achieve unprecedented levels of performance, the XAI domain aims at making these models understandable by presenting end-users with intelligible explanations. Yet, some existing XAI approaches fail to meet expectations: several issues have been reported in the literature, generally pointing out either technical limitations or misinterpretations by users. In this paper, we argue that the resulting harms arise from a complex overlap of multiple failures in XAI, which existing ad-hoc studies fail to capture. This work therefore advocates for a holistic perspective, presenting a systematic investigation of limitations of current XAI methods and their impact on the interpretation of explanations. By distinguishing between system-specific and user-specific failures, we propose a typological framework that helps revealing the nuanced complexities of explanation failures. Leveraging this typology, we also discuss some research directions to help AI practitioners better understand the limitations of XAI systems and enhance the quality of ML explanations. | [
"['Clara Bove' 'Thibault Laugel' 'Marie-Jeanne Lesot' 'Charles Tijus'\n 'Marcin Detyniecki']"
] |
null | null | 2405.13481 | null | null | http://arxiv.org/pdf/2405.13481v1 | 2024-05-22T09:47:54Z | 2024-05-22T09:47:54Z | Locally Private Estimation with Public Features | We initiate the study of locally differentially private (LDP) learning with public features. We define semi-feature LDP, where some features are publicly available while the remaining ones, along with the label, require protection under local differential privacy. Under semi-feature LDP, we demonstrate that the mini-max convergence rate for non-parametric regression is significantly reduced compared to that of classical LDP. Then we propose HistOfTree, an estimator that fully leverages the information contained in both public and private features. Theoretically, HistOfTree reaches the mini-max optimal convergence rate. Empirically, HistOfTree achieves superior performance on both synthetic and real data. We also explore scenarios where users have the flexibility to select features for protection manually. In such cases, we propose an estimator and a data-driven parameter tuning strategy, leading to analogous theoretical and empirical results. | [
"['Yuheng Ma' 'Ke Jia' 'Hanfang Yang']"
] |
null | null | 2405.13511 | null | null | http://arxiv.org/pdf/2405.13511v2 | 2024-06-04T10:13:13Z | 2024-05-22T10:12:32Z | Latent Space Alignment for Semantic Channel Equalization | We relax the constraint of a shared language between agents in a semantic and goal-oriented communication system to explore the effect of language mismatch in distributed task solving. We propose a mathematical framework, which provides a modelling and a measure of the semantic distortion introduced in the communication when agents use distinct languages. We then propose a new approach to semantic channel equalization with proven effectiveness through numerical evaluations. | [
"['Tomás Hüttebräucker' 'Mohamed Sana' 'Emilio Calvanese Strinati']"
] |
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