categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2407.00106 | null | null | http://arxiv.org/pdf/2407.00106v1 | 2024-06-27T10:24:35Z | 2024-06-27T10:24:35Z | UnUnlearning: Unlearning is not sufficient for content regulation in
advanced generative AI | Exact unlearning was first introduced as a privacy mechanism that allowed a user to retract their data from machine learning models on request. Shortly after, inexact schemes were proposed to mitigate the impractical costs associated with exact unlearning. More recently unlearning is often discussed as an approach for removal of impermissible knowledge i.e. knowledge that the model should not possess such as unlicensed copyrighted, inaccurate, or malicious information. The promise is that if the model does not have a certain malicious capability, then it cannot be used for the associated malicious purpose. In this paper we revisit the paradigm in which unlearning is used for in Large Language Models (LLMs) and highlight an underlying inconsistency arising from in-context learning. Unlearning can be an effective control mechanism for the training phase, yet it does not prevent the model from performing an impermissible act during inference. We introduce a concept of ununlearning, where unlearned knowledge gets reintroduced in-context, effectively rendering the model capable of behaving as if it knows the forgotten knowledge. As a result, we argue that content filtering for impermissible knowledge will be required and even exact unlearning schemes are not enough for effective content regulation. We discuss feasibility of ununlearning for modern LLMs and examine broader implications. | [
"['Ilia Shumailov' 'Jamie Hayes' 'Eleni Triantafillou'\n 'Guillermo Ortiz-Jimenez' 'Nicolas Papernot' 'Matthew Jagielski'\n 'Itay Yona' 'Heidi Howard' 'Eugene Bagdasaryan']"
] |
null | null | 2407.00107 | null | null | http://arxiv.org/pdf/2407.00107v2 | 2024-07-11T12:12:48Z | 2024-06-27T11:11:19Z | WineGraph: A Graph Representation For Food-Wine Pairing | We present WineGraph, an extended version of FlavorGraph, a heterogeneous graph incorporating wine data into its structure. This integration enables food-wine pairing based on taste and sommelier-defined rules. Leveraging a food dataset comprising 500,000 reviews and a wine reviews dataset with over 130,000 entries, we computed taste descriptors for both food and wine. This information was then utilised to pair food items with wine and augment FlavorGraph with additional data. The results demonstrate the potential of heterogeneous graphs to acquire supplementary information, proving beneficial for wine pairing. | [
"['Zuzanna Gawrysiak' 'Agata Żywot' 'Agnieszka Ławrynowicz']"
] |
null | null | 2407.00108 | null | null | http://arxiv.org/pdf/2407.00108v1 | 2024-06-27T11:20:14Z | 2024-06-27T11:20:14Z | A Case Study on Contextual Machine Translation in a Professional
Scenario of Subtitling | Incorporating extra-textual context such as film metadata into the machine translation (MT) pipeline can enhance translation quality, as indicated by automatic evaluation in recent work. However, the positive impact of such systems in industry remains unproven. We report on an industrial case study carried out to investigate the benefit of MT in a professional scenario of translating TV subtitles with a focus on how leveraging extra-textual context impacts post-editing. We found that post-editors marked significantly fewer context-related errors when correcting the outputs of MTCue, the context-aware model, as opposed to non-contextual models. We also present the results of a survey of the employed post-editors, which highlights contextual inadequacy as a significant gap consistently observed in MT. Our findings strengthen the motivation for further work within fully contextual MT. | [
"['Sebastian Vincent' 'Charlotte Prescott' 'Chris Bayliss' 'Chris Oakley'\n 'Carolina Scarton']"
] |
null | null | 2407.00111 | null | null | http://arxiv.org/pdf/2407.00111v1 | 2024-06-27T13:04:58Z | 2024-06-27T13:04:58Z | Accurate Prediction of Ligand-Protein Interaction Affinities with
Fine-Tuned Small Language Models | We describe the accurate prediction of ligand-protein interaction (LPI) affinities, also known as drug-target interactions (DTI), with instruction fine-tuned pretrained generative small language models (SLMs). We achieved accurate predictions for a range of affinity values associated with ligand-protein interactions on out-of-sample data in a zero-shot setting. Only the SMILES string of the ligand and the amino acid sequence of the protein were used as the model inputs. Our results demonstrate a clear improvement over machine learning (ML) and free-energy perturbation (FEP+) based methods in accurately predicting a range of ligand-protein interaction affinities, which can be leveraged to further accelerate drug discovery campaigns against challenging therapeutic targets. | [
"['Ben Fauber']"
] |
null | null | 2407.00113 | null | null | http://arxiv.org/abs/2407.00113v1 | 2024-06-27T13:41:37Z | 2024-06-27T13:41:37Z | Personalized Federated Continual Learning via Multi-granularity Prompt | Personalized Federated Continual Learning (PFCL) is a new practical scenario that poses greater challenges in sharing and personalizing knowledge. PFCL not only relies on knowledge fusion for server aggregation at the global spatial-temporal perspective but also needs model improvement for each client according to the local requirements. Existing methods, whether in Personalized Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked the multi-granularity representation of knowledge, which can be utilized to overcome Spatial-Temporal Catastrophic Forgetting (STCF) and adopt generalized knowledge to itself by coarse-to-fine human cognitive mechanisms. Moreover, it allows more effectively to personalized shared knowledge, thus serving its own purpose. To this end, we propose a novel concept called multi-granularity prompt, i.e., coarse-grained global prompt acquired through the common model learning process, and fine-grained local prompt used to personalize the generalized representation. The former focuses on efficiently transferring shared global knowledge without spatial forgetting, and the latter emphasizes specific learning of personalized local knowledge to overcome temporal forgetting. In addition, we design a selective prompt fusion mechanism for aggregating knowledge of global prompts distilled from different clients. By the exclusive fusion of coarse-grained knowledge, we achieve the transmission and refinement of common knowledge among clients, further enhancing the performance of personalization. Extensive experiments demonstrate the effectiveness of the proposed method in addressing STCF as well as improving personalized performance. Our code now is available at https://github.com/SkyOfBeginning/FedMGP. | [
"['Hao Yu' 'Xin Yang' 'Xin Gao' 'Yan Kang' 'Hao Wang' 'Junbo Zhang'\n 'Tianrui Li']"
] |
null | null | 2407.00114 | null | null | http://arxiv.org/pdf/2407.00114v1 | 2024-06-27T13:46:11Z | 2024-06-27T13:46:11Z | OmniJARVIS: Unified Vision-Language-Action Tokenization Enables
Open-World Instruction Following Agents | We present OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in open-world Minecraft. Compared to prior works that either emit textual goals to separate controllers or produce the control command directly, OmniJARVIS seeks a different path to ensure both strong reasoning and efficient decision-making capabilities via unified tokenization of multimodal interaction data. First, we introduce a self-supervised approach to learn a behavior encoder that produces discretized tokens for behavior trajectories $tau$ = {$o_0$, $a_0$, $dots$} and an imitation learning (IL) policy decoder conditioned on these tokens. These additional behavior tokens will be augmented to the vocabulary of pretrained Multimodal Language Models (MLMs). With this encoder, we then pack long-term multimodal interactions involving task instructions, memories, thoughts, observations, textual responses, behavior trajectories, etc. into unified token sequences and model them with autoregressive transformers. Thanks to the semantically meaningful behavior tokens, the resulting VLA model, OmniJARVIS, can reason (by producing chain-of-thoughts), plan, answer questions, and act (by producing behavior tokens for the IL policy decoder). OmniJARVIS demonstrates excellent performances on a comprehensive collection of atomic, programmatic, and open-ended tasks in open-world Minecraft. Our analysis further unveils the crucial design principles in interaction data formation, unified tokenization, and its scaling potentials. | [
"['Zihao Wang' 'Shaofei Cai' 'Zhancun Mu' 'Haowei Lin' 'Ceyao Zhang'\n 'Xuejie Liu' 'Qing Li' 'Anji Liu' 'Xiaojian Ma' 'Yitao Liang']"
] |
null | null | 2407.00115 | null | null | http://arxiv.org/pdf/2407.00115v3 | 2024-07-07T15:25:05Z | 2024-06-27T14:00:05Z | Instance Temperature Knowledge Distillation | Knowledge distillation (KD) enhances the performance of a student network by allowing it to learn the knowledge transferred from a teacher network incrementally. Existing methods dynamically adjust the temperature to enable the student network to adapt to the varying learning difficulties at different learning stages of KD. KD is a continuous process, but when adjusting the temperature, these methods consider only the immediate benefits of the operation in the current learning phase and fail to take into account its future returns. To address this issue, we formulate the adjustment of temperature as a sequential decision-making task and propose a method based on reinforcement learning, termed RLKD. Importantly, we design a novel state representation to enable the agent to make more informed action (i.e. instance temperature adjustment). To handle the problem of delayed rewards in our method due to the KD setting, we explore an instance reward calibration approach. In addition,we devise an efficient exploration strategy that enables the agent to learn valuable instance temperature adjustment policy more efficiently. Our framework can serve as a plug-and-play technique to be inserted into various KD methods easily, and we validate its effectiveness on both image classification and object detection tasks. Our project is at https://www.zayx.me/ITKD.github.io/. | [
"['Zhengbo Zhang' 'Yuxi Zhou' 'Jia Gong' 'Jun Liu' 'Zhigang Tu']"
] |
null | null | 2407.00116 | null | null | http://arxiv.org/pdf/2407.00116v2 | 2024-07-02T06:51:09Z | 2024-06-27T14:00:11Z | Generative AI for Synthetic Data Across Multiple Medical Modalities: A
Systematic Review of Recent Developments and Challenges | This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, and tabular data (EHR). Unlike previous narrowly focused reviews, our study encompasses a broad array of medical data modalities and explores various generative models. Our search strategy queries databases such as Scopus, PubMed, and ArXiv, focusing on recent works from January 2021 to November 2023, excluding reviews and perspectives. This period emphasizes recent advancements beyond GANs, which have been extensively covered previously. The survey reveals insights from three key aspects: (1) Synthesis applications and purpose of synthesis, (2) generation techniques, and (3) evaluation methods. It highlights clinically valid synthesis applications, demonstrating the potential of synthetic data to tackle diverse clinical requirements. While conditional models incorporating class labels, segmentation masks and image translations are prevalent, there is a gap in utilizing prior clinical knowledge and patient-specific context, suggesting a need for more personalized synthesis approaches and emphasizing the importance of tailoring generative approaches to the unique characteristics of medical data. Additionally, there is a significant gap in using synthetic data beyond augmentation, such as for validation and evaluation of downstream medical AI models. The survey uncovers that the lack of standardized evaluation methodologies tailored to medical images is a barrier to clinical application, underscoring the need for in-depth evaluation approaches, benchmarking, and comparative studies to promote openness and collaboration. | [
"['Mahmoud Ibrahim' 'Yasmina Al Khalil' 'Sina Amirrajab' 'Chang Sun'\n 'Marcel Breeuwer' 'Josien Pluim' 'Bart Elen' 'Gokhan Ertaylan'\n 'Michel Dumontier']"
] |
null | null | 2407.00117 | null | null | http://arxiv.org/pdf/2407.00117v1 | 2024-06-27T14:18:23Z | 2024-06-27T14:18:23Z | Machine learning meets mass spectrometry: a focused perspective | Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, catalysis, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or a part of tandem mass spectrometry experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with mass spectrometry studies. Consequently, mass spectrometry has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information mass spectrometry experiments could provide. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. The present perspective highlights reevaluation of mass spectrometry data analysis in the new generation of methods and describes significant challenges in the field, particularly related to problems involving the use of electrospray ionization. We argue that further applications of machine learning raise new requirements for instrumentation (increasing throughput and information density, decreasing pricing, and making more automation-friendly software), and once met, the field may experience significant transformation. | [
"['Daniil A. Boiko' 'Valentine P. Ananikov']"
] |
null | null | 2407.00118 | null | null | http://arxiv.org/pdf/2407.00118v1 | 2024-06-27T15:36:43Z | 2024-06-27T15:36:43Z | From Efficient Multimodal Models to World Models: A Survey | Multimodal Large Models (MLMs) are becoming a significant research focus, combining powerful large language models with multimodal learning to perform complex tasks across different data modalities. This review explores the latest developments and challenges in MLMs, emphasizing their potential in achieving artificial general intelligence and as a pathway to world models. We provide an overview of key techniques such as Multimodal Chain of Thought (M-COT), Multimodal Instruction Tuning (M-IT), and Multimodal In-Context Learning (M-ICL). Additionally, we discuss both the fundamental and specific technologies of multimodal models, highlighting their applications, input/output modalities, and design characteristics. Despite significant advancements, the development of a unified multimodal model remains elusive. We discuss the integration of 3D generation and embodied intelligence to enhance world simulation capabilities and propose incorporating external rule systems for improved reasoning and decision-making. Finally, we outline future research directions to address these challenges and advance the field. | [
"['Xinji Mai' 'Zeng Tao' 'Junxiong Lin' 'Haoran Wang' 'Yang Chang'\n 'Yanlan Kang' 'Yan Wang' 'Wenqiang Zhang']"
] |
null | null | 2407.00119 | null | null | http://arxiv.org/pdf/2407.00119v1 | 2024-06-27T15:54:12Z | 2024-06-27T15:54:12Z | Efficient Long-distance Latent Relation-aware Graph Neural Network for
Multi-modal Emotion Recognition in Conversations | The task of multi-modal emotion recognition in conversation (MERC) aims to analyze the genuine emotional state of each utterance based on the multi-modal information in the conversation, which is crucial for conversation understanding. Existing methods focus on using graph neural networks (GNN) to model conversational relationships and capture contextual latent semantic relationships. However, due to the complexity of GNN, existing methods cannot efficiently capture the potential dependencies between long-distance utterances, which limits the performance of MERC. In this paper, we propose an Efficient Long-distance Latent Relation-aware Graph Neural Network (ELR-GNN) for multi-modal emotion recognition in conversations. Specifically, we first use pre-extracted text, video and audio features as input to Bi-LSTM to capture contextual semantic information and obtain low-level utterance features. Then, we use low-level utterance features to construct a conversational emotion interaction graph. To efficiently capture the potential dependencies between long-distance utterances, we use the dilated generalized forward push algorithm to precompute the emotional propagation between global utterances and design an emotional relation-aware operator to capture the potential semantic associations between different utterances. Furthermore, we combine early fusion and adaptive late fusion mechanisms to fuse latent dependency information between speaker relationship information and context. Finally, we obtain high-level discourse features and feed them into MLP for emotion prediction. Extensive experimental results show that ELR-GNN achieves state-of-the-art performance on the benchmark datasets IEMOCAP and MELD, with running times reduced by 52% and 35%, respectively. | [
"['Yuntao Shou' 'Wei Ai' 'Jiayi Du' 'Tao Meng' 'Haiyan Liu']"
] |
null | null | 2407.00120 | null | null | http://arxiv.org/pdf/2407.00120v1 | 2024-06-27T16:50:36Z | 2024-06-27T16:50:36Z | Automated Web-Based Malaria Detection System with Machine Learning and
Deep Learning Techniques | Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is crucial for effective treatment and control. However, existing automated detection techniques have shown limitations in terms of accuracy and generalizability. Many studies have focused on specific features without exploring more comprehensive approaches. In our case, we formulate a deep learning technique for malaria-infected cell classification using traditional CNNs and transfer learning models notably VGG19, InceptionV3, and Xception. The models were trained using NIH datasets and tested using different performance metrics such as accuracy, precision, recall, and F1-score. The test results showed that deep CNNs achieved the highest accuracy -- 97%, followed by Xception with an accuracy of 95%. A machine learning model SVM achieved an accuracy of 83%, while an Inception-V3 achieved an accuracy of 94%. Furthermore, the system can be accessed through a web interface, where users can upload blood smear images for malaria detection. | [
"['Abraham G Taye' 'Sador Yemane' 'Eshetu Negash' 'Yared Minwuyelet'\n 'Moges Abebe' 'Melkamu Hunegnaw Asmare']"
] |
null | null | 2407.00121 | null | null | http://arxiv.org/pdf/2407.00121v1 | 2024-06-27T17:47:26Z | 2024-06-27T17:47:26Z | Granite-Function Calling Model: Introducing Function Calling Abilities
via Multi-task Learning of Granular Tasks | Large language models (LLMs) have recently shown tremendous promise in serving as the backbone to agentic systems, as demonstrated by their performance in multi-faceted, challenging benchmarks like SWE-Bench and Agent-Bench. However, to realize the true potential of LLMs as autonomous agents, they must learn to identify, call, and interact with external tools and application program interfaces (APIs) to complete complex tasks. These tasks together are termed function calling. Endowing LLMs with function calling abilities leads to a myriad of advantages, such as access to current and domain-specific information in databases and knowledge sources, and the ability to outsource tasks that can be reliably performed by tools, e.g., a Python interpreter or calculator. While there has been significant progress in function calling with LLMs, there is still a dearth of open models that perform on par with proprietary LLMs like GPT, Claude, and Gemini. Therefore, in this work, we introduce the GRANITE-20B-FUNCTIONCALLING model under an Apache 2.0 license. The model is trained using a multi-task training approach on seven fundamental tasks encompassed in function calling, those being Nested Function Calling, Function Chaining, Parallel Functions, Function Name Detection, Parameter-Value Pair Detection, Next-Best Function, and Response Generation. We present a comprehensive evaluation on multiple out-of-domain datasets comparing GRANITE-20B-FUNCTIONCALLING to more than 15 other best proprietary and open models. GRANITE-20B-FUNCTIONCALLING provides the best performance among all open models on the Berkeley Function Calling Leaderboard and fourth overall. As a result of the diverse tasks and datasets used for training our model, we show that GRANITE-20B-FUNCTIONCALLING has better generalizability on multiple tasks in seven different evaluation datasets. | [
"['Ibrahim Abdelaziz' 'Kinjal Basu' 'Mayank Agarwal' 'Sadhana Kumaravel'\n 'Matthew Stallone' 'Rameswar Panda' 'Yara Rizk' 'GP Bhargav'\n 'Maxwell Crouse' 'Chulaka Gunasekara' 'Shajith Ikbal' 'Sachin Joshi'\n 'Hima Karanam' 'Vineet Kumar' 'Asim Munawar' 'Sumit Neelam'\n 'Dinesh Raghu' 'Udit Sharma' 'Adriana Meza Soria' 'Dheeraj Sreedhar'\n 'Praveen Venkateswaran' 'Merve Unuvar' 'David Cox' 'Salim Roukos'\n 'Luis Lastras' 'Pavan Kapanipathi']"
] |
null | null | 2407.00127 | null | null | http://arxiv.org/pdf/2407.00127v1 | 2024-06-28T03:03:55Z | 2024-06-28T03:03:55Z | Multi-Species Object Detection in Drone Imagery for Population
Monitoring of Endangered Animals | Animal populations worldwide are rapidly declining, and a technology that can accurately count endangered species could be vital for monitoring population changes over several years. This research focused on fine-tuning object detection models for drone images to create accurate counts of animal species. Hundreds of images taken using a drone and large, openly available drone-image datasets were used to fine-tune machine learning models with the baseline YOLOv8 architecture. We trained 30 different models, with the largest having 43.7 million parameters and 365 layers, and used hyperparameter tuning and data augmentation techniques to improve accuracy. While the state-of-the-art YOLOv8 baseline had only 0.7% accuracy on a dataset of safari animals, our models had 95% accuracy on the same dataset. Finally, we deployed the models on the Jetson Orin Nano for demonstration of low-power real-time species detection for easy inference on drones. | [
"['Sowmya Sankaran']"
] |
null | null | 2407.00128 | null | null | http://arxiv.org/pdf/2407.00128v1 | 2024-06-28T03:52:13Z | 2024-06-28T03:52:13Z | When Search Engine Services meet Large Language Models: Visions and
Challenges | Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we search for and retrieve information, understand content, and interact with internet services. This paper conducts an in-depth examination of how integrating LLMs with search engines can mutually benefit both technologies. We focus on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engine functions using LLMs (LLM4Search). For Search4LLM, we investigate how search engines can provide diverse high-quality datasets for pre-training of LLMs, how they can use the most relevant documents to help LLMs learn to answer queries more accurately, how training LLMs with Learning-To-Rank (LTR) tasks can enhance their ability to respond with greater precision, and how incorporating recent search results can make LLM-generated content more accurate and current. In terms of LLM4Search, we examine how LLMs can be used to summarize content for better indexing by search engines, improve query outcomes through optimization, enhance the ranking of search results by analyzing document relevance, and help in annotating data for learning-to-rank tasks in various learning contexts. However, this promising integration comes with its challenges, which include addressing potential biases and ethical issues in training models, managing the computational and other costs of incorporating LLMs into search services, and continuously updating LLM training with the ever-changing web content. We discuss these challenges and chart out required research directions to address them. We also discuss broader implications for service computing, such as scalability, privacy concerns, and the need to adapt search engine architectures for these advanced models. | [
"['Haoyi Xiong' 'Jiang Bian' 'Yuchen Li' 'Xuhong Li' 'Mengnan Du'\n 'Shuaiqiang Wang' 'Dawei Yin' 'Sumi Helal']"
] |
null | null | 2407.00131 | null | null | http://arxiv.org/pdf/2407.00131v1 | 2024-06-28T08:25:45Z | 2024-06-28T08:25:45Z | RepAct: The Re-parameterizable Adaptive Activation Function | Addressing the imperative need for efficient artificial intelligence in IoT and edge computing, this study presents RepAct, a re-parameterizable adaptive activation function tailored for optimizing lightweight neural networks within the computational limitations of edge devices. By employing a multi-branch structure with learnable adaptive weights, RepAct enriches feature processing and enhances cross-layer interpretability. When evaluated on tasks such as image classification and object detection, RepAct notably surpassed conventional activation functions in lightweight networks, delivering up to a 7.92% accuracy boost on MobileNetV3-Small for the ImageNet100 dataset, while maintaining computational complexity on par with HardSwish. This innovative approach not only maximizes model parameter efficiency but also significantly improves the performance and understanding capabilities of lightweight neural networks, demonstrating its potential for real-time edge computing applications. | [
"['Xian Wu' 'Qingchuan Tao' 'Shuang Wang']"
] |
null | null | 2407.00134 | null | null | http://arxiv.org/pdf/2407.00134v1 | 2024-06-28T10:43:02Z | 2024-06-28T10:43:02Z | A Simple Attention-Based Mechanism for Bimodal Emotion Classification | Big data contain rich information for machine learning algorithms to utilize when learning important features during classification tasks. Human beings express their emotion using certain words, speech (tone, pitch, speed) or facial expression. Artificial Intelligence approach to emotion classification are largely based on learning from textual information. However, public datasets containing text and speech data provide sufficient resources to train machine learning algorithms for the tack of emotion classification. In this paper, we present novel bimodal deep learning-based architectures enhanced with attention mechanism trained and tested on text and speech data for emotion classification. We report details of different deep learning based architectures and show the performance of each architecture including rigorous error analyses. Our finding suggests that deep learning based architectures trained on different types of data (text and speech) outperform architectures trained only on text or speech. Our proposed attention-based bimodal architecture outperforms several state-of-the-art systems in emotion classification. | [
"['Mazen Elabd' 'Sardar Jaf']"
] |
null | null | 2407.00140 | null | null | http://arxiv.org/pdf/2407.00140v1 | 2024-06-28T14:46:17Z | 2024-06-28T14:46:17Z | ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal
Structural Behavior | External influences such as traffic and environmental factors induce vibrations in structures, leading to material degradation over time. These vibrations result in cracks due to the material's lack of plasticity compromising structural integrity. Detecting such damage requires the installation of vibration sensors to capture the internal dynamics. However, distinguishing relevant eigenmodes from external noise necessitates the use of Deep Learning models. The detection of changes in eigenmodes can be used to anticipate these shifts in material properties and to discern between normal and anomalous structural behavior. Eigenmodes, representing characteristic vibration patterns, provide insights into structural dynamics and deviations from expected states. Thus, we propose ModeConv to automatically capture and analyze changes in eigenmodes, facilitating effective anomaly detection in structures and material properties. In the conducted experiments, ModeConv demonstrates computational efficiency improvements, resulting in reduced runtime for model calculations. The novel ModeConv neural network layer is tailored for temporal graph neural networks, in which every node represents one sensor. ModeConv employs a singular value decomposition based convolutional filter design for complex numbers and leverages modal transformation in lieu of Fourier or Laplace transformations in spectral graph convolutions. We include a mathematical complexity analysis illustrating the runtime reduction. | [
"['Melanie Schaller' 'Daniel Schlör' 'Andreas Hotho']"
] |
null | null | 2407.00141 | null | null | http://arxiv.org/pdf/2407.00141v1 | 2024-06-28T15:20:50Z | 2024-06-28T15:20:50Z | Towards Secure and Efficient Data Scheduling for Vehicular Social
Networks | Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks. Contemporary research predominantly centers on crafting cooperative scheduling algorithms tailored for vehicular networks. Notwithstanding, the intricacies of orchestrating scheduling in vehicular social networks both effectively and efficiently remain formidable. This paper introduces an innovative learning-based algorithm for scheduling data transmission that prioritizes efficiency and security within vehicular social networks. The algorithm first uses a specifically constructed neural network to enhance data processing capabilities. After this, it incorporates a Q-learning paradigm during the data transmission phase to optimize the information exchange, the privacy of which is safeguarded by differential privacy through the communication process. Comparative experiments demonstrate the superior performance of the proposed Q-learning enhanced scheduling algorithm relative to existing state-of-the-art scheduling algorithms in the context of vehicular social networks. | [
"['Youhua Xia' 'Tiehua Zhang' 'Jiong Jin' 'Ying He' 'Fei Yu']"
] |
null | null | 2407.00142 | null | null | http://arxiv.org/pdf/2407.00142v1 | 2024-06-28T15:53:36Z | 2024-06-28T15:53:36Z | Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary
work | The gut microbiome, crucial for human health, presents challenges in analyzing its complex metaomic data due to high dimensionality and sparsity. Traditional methods struggle to capture its intricate relationships. We investigate graph neural networks (GNNs) for this task, aiming to derive meaningful representations of individual gut microbiomes. Unlike methods relying solely on taxa abundance, we directly leverage phylogenetic relationships, in order to obtain a generalized encoder for taxa networks. The representation learnt from the encoder are then used to train a model for phenotype prediction such as Inflammatory Bowel Disease (IBD). | [
"['Christopher Irwin' 'Flavio Mignone' 'Stefania Montani' 'Luigi Portinale']"
] |
null | null | 2407.00143 | null | null | http://arxiv.org/pdf/2407.00143v1 | 2024-06-28T16:08:26Z | 2024-06-28T16:08:26Z | InfoNCE: Identifying the Gap Between Theory and Practice | Previous theoretical work on contrastive learning (CL) with InfoNCE showed that, under certain assumptions, the learned representations uncover the ground-truth latent factors. We argue these theories overlook crucial aspects of how CL is deployed in practice. Specifically, they assume that within a positive pair, all latent factors either vary to a similar extent, or that some do not vary at all. However, in practice, positive pairs are often generated using augmentations such as strong cropping to just a few pixels. Hence, a more realistic assumption is that all latent factors change, with a continuum of variability across these factors. We introduce AnInfoNCE, a generalization of InfoNCE that can provably uncover the latent factors in this anisotropic setting, broadly generalizing previous identifiability results in CL. We validate our identifiability results in controlled experiments and show that AnInfoNCE increases the recovery of previously collapsed information in CIFAR10 and ImageNet, albeit at the cost of downstream accuracy. Additionally, we explore and discuss further mismatches between theoretical assumptions and practical implementations, including extensions to hard negative mining and loss ensembles. | [
"['Evgenia Rusak' 'Patrik Reizinger' 'Attila Juhos' 'Oliver Bringmann'\n 'Roland S. Zimmermann' 'Wieland Brendel']"
] |
null | null | 2407.00147 | null | null | http://arxiv.org/pdf/2407.00147v1 | 2024-06-28T17:01:12Z | 2024-06-28T17:01:12Z | Predicting Elevated Risk of Hospitalization Following Emergency
Department Discharges | Hospitalizations that follow closely on the heels of one or more emergency department visits are often symptoms of missed opportunities to form a proper diagnosis. These diagnostic errors imply a failure to recognize the need for hospitalization and deliver appropriate care, and thus also bear important connotations for patient safety. In this paper, we show how data mining techniques can be applied to a large existing hospitalization data set to learn useful models that predict these upcoming hospitalizations with high accuracy. Specifically, we use an ensemble of logistics regression, na"ive Bayes and association rule classifiers to successfully predict hospitalization within 3, 7 and 14 days of an emergency department discharge. Aside from high accuracy, one of the advantages of the techniques proposed here is that the resulting classifier is easily inspected and interpreted by humans so that the learned rules can be readily operationalized. These rules can then be easily distributed and applied directly by physicians in emergency department settings to predict the risk of early admission prior to discharging their emergency department patients. | [
"['Dat Hong' 'Philip M. Polgreen' 'Alberto Maria Segre']"
] |
null | null | 2407.00148 | null | null | http://arxiv.org/pdf/2407.00148v1 | 2024-06-28T17:57:12Z | 2024-06-28T17:57:12Z | Localizing Anomalies via Multiscale Score Matching Analysis | Anomaly detection and localization in medical imaging remain critical challenges in healthcare. This paper introduces Spatial-MSMA (Multiscale Score Matching Analysis), a novel unsupervised method for anomaly localization in volumetric brain MRIs. Building upon the MSMA framework, our approach incorporates spatial information and conditional likelihoods to enhance anomaly detection capabilities. We employ a flexible normalizing flow model conditioned on patch positions and global image features to estimate patch-wise anomaly scores. The method is evaluated on a dataset of 1,650 T1- and T2-weighted brain MRIs from typically developing children, with simulated lesions added to the test set. Spatial-MSMA significantly outperforms existing methods, including reconstruction-based, generative-based, and interpretation-based approaches, in lesion detection and segmentation tasks. Our model achieves superior performance in both distance-based metrics (99th percentile Hausdorff Distance: $7.05 pm 0.61$, Mean Surface Distance: $2.10 pm 0.43$) and component-wise metrics (True Positive Rate: $0.83 pm 0.01$, Positive Predictive Value: $0.96 pm 0.01$). These results demonstrate Spatial-MSMA's potential for accurate and interpretable anomaly localization in medical imaging, with implications for improved diagnosis and treatment planning in clinical settings. Our code is available at~url{https://github.com/ahsanMah/sade/}. | [
"['Ahsan Mahmood' 'Junier Oliva' 'Martin Styner']"
] |
null | null | 2407.00170 | null | null | http://arxiv.org/pdf/2407.00170v1 | 2024-06-28T18:11:16Z | 2024-06-28T18:11:16Z | Dataset Representativeness and Downstream Task Fairness | Our society collects data on people for a wide range of applications, from building a census for policy evaluation to running meaningful clinical trials. To collect data, we typically sample individuals with the goal of accurately representing a population of interest. However, current sampling processes often collect data opportunistically from data sources, which can lead to datasets that are biased and not representative, i.e., the collected dataset does not accurately reflect the distribution of demographics of the true population. This is a concern because subgroups within the population can be under- or over-represented in a dataset, which may harm generalizability and lead to an unequal distribution of benefits and harms from downstream tasks that use such datasets (e.g., algorithmic bias in medical decision-making algorithms). In this paper, we assess the relationship between dataset representativeness and group-fairness of classifiers trained on that dataset. We demonstrate that there is a natural tension between dataset representativeness and classifier fairness; empirically we observe that training datasets with better representativeness can frequently result in classifiers with higher rates of unfairness. We provide some intuition as to why this occurs via a set of theoretical results in the case of univariate classifiers. We also find that over-sampling underrepresented groups can result in classifiers which exhibit greater bias to those groups. Lastly, we observe that fairness-aware sampling strategies (i.e., those which are specifically designed to select data with high downstream fairness) will often over-sample members of majority groups. These results demonstrate that the relationship between dataset representativeness and downstream classifier fairness is complex; balancing these two quantities requires special care from both model- and dataset-designers. | [
"['Victor Borza' 'Andrew Estornell' 'Chien-Ju Ho' 'Bradley Malin'\n 'Yevgeniy Vorobeychik']"
] |
null | null | 2407.00175 | null | null | http://arxiv.org/pdf/2407.00175v1 | 2024-06-28T18:28:38Z | 2024-06-28T18:28:38Z | Permutation invariant multi-output Gaussian Processes for drug
combination prediction in cancer | Dose-response prediction in cancer is an active application field in machine learning. Using large libraries of textit{in-vitro} drug sensitivity screens, the goal is to develop accurate predictive models that can be used to guide experimental design or inform treatment decisions. Building on previous work that makes use of permutation invariant multi-output Gaussian Processes in the context of dose-response prediction for drug combinations, we develop a variational approximation to these models. The variational approximation enables a more scalable model that provides uncertainty quantification and naturally handles missing data. Furthermore, we propose using a deep generative model to encode the chemical space in a continuous manner, enabling prediction for new drugs and new combinations. We demonstrate the performance of our model in a simple setting using a high-throughput dataset and show that the model is able to efficiently borrow information across outputs. | [
"['Leiv Rønneberg' 'Vidhi Lalchand' 'Paul D. W. Kirk']"
] |
null | null | 2407.00176 | null | null | http://arxiv.org/pdf/2407.00176v1 | 2024-06-28T18:29:51Z | 2024-06-28T18:29:51Z | The impact of model size on catastrophic forgetting in Online Continual
Learning | This study investigates the impact of model size on Online Continual Learning performance, with a focus on catastrophic forgetting. Employing ResNet architectures of varying sizes, the research examines how network depth and width affect model performance in class-incremental learning using the SplitCIFAR-10 dataset. Key findings reveal that larger models do not guarantee better Continual Learning performance; in fact, they often struggle more in adapting to new tasks, particularly in online settings. These results challenge the notion that larger models inherently mitigate catastrophic forgetting, highlighting the nuanced relationship between model size and Continual Learning efficacy. This study contributes to a deeper understanding of model scalability and its practical implications in Continual Learning scenarios. | [
"['Eunhae Lee']"
] |
null | null | 2407.00186 | null | null | http://arxiv.org/pdf/2407.00186v1 | 2024-06-28T18:52:11Z | 2024-06-28T18:52:11Z | DCSM 2.0: Deep Conditional Shape Models for Data Efficient Segmentation | Segmentation is often the first step in many medical image analyses workflows. Deep learning approaches, while giving state-of-the-art accuracies, are data intensive and do not scale well to low data regimes. We introduce Deep Conditional Shape Models 2.0, which uses an edge detector, along with an implicit shape function conditioned on edge maps, to leverage cross-modality shape information. The shape function is trained exclusively on a source domain (contrasted CT) and applied to the target domain of interest (3D echocardiography). We demonstrate data efficiency in the target domain by varying the amounts of training data used in the edge detection stage. We observe that DCSM 2.0 outperforms the baseline at all data levels in terms of Hausdorff distances, and while using 50% or less of the training data in terms of average mesh distance, and at 10% or less of the data with the dice coefficient. The method scales well to low data regimes, with gains of up to 5% in dice coefficient, 2.58 mm in average surface distance and 21.02 mm in Hausdorff distance when using just 2% (22 volumes) of the training data. | [
"['Athira J Jacob' 'Puneet Sharma' 'Daniel Rueckert']"
] |
null | null | 2407.00197 | null | null | http://arxiv.org/pdf/2407.00197v1 | 2024-06-28T19:09:55Z | 2024-06-28T19:09:55Z | Tradeoffs When Considering Deep Reinforcement Learning for Contingency
Management in Advanced Air Mobility | Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process. | [
"['Luis E. Alvarez' 'Marc W. Brittain' 'Steven D. Young']"
] |
null | null | 2407.00201 | null | null | http://arxiv.org/pdf/2407.00201v2 | 2024-07-03T15:37:54Z | 2024-06-28T19:13:48Z | Deconvolving Complex Neuronal Networks into Interpretable Task-Specific
Connectomes | Task-specific functional MRI (fMRI) images provide excellent modalities for studying the neuronal basis of cognitive processes. We use fMRI data to formulate and solve the problem of deconvolving task-specific aggregate neuronal networks into a set of basic building blocks called canonical networks, to use these networks for functional characterization, and to characterize the physiological basis of these responses by mapping them to regions of the brain. Our results show excellent task-specificity of canonical networks, i.e., the expression of a small number of canonical networks can be used to accurately predict tasks; generalizability across cohorts, i.e., canonical networks are conserved across diverse populations, studies, and acquisition protocols; and that canonical networks have strong anatomical and physiological basis. From a methods perspective, the problem of identifying these canonical networks poses challenges rooted in the high dimensionality, small sample size, acquisition variability, and noise. Our deconvolution technique is based on non-negative matrix factorization (NMF) that identifies canonical networks as factors of a suitably constructed matrix. We demonstrate that our method scales to large datasets, yields stable and accurate factors, and is robust to noise. | [
"['Yifan Wang' 'Vikram Ravindra' 'Ananth Grama']"
] |
null | null | 2407.00215 | null | null | http://arxiv.org/pdf/2407.00215v1 | 2024-06-28T19:53:17Z | 2024-06-28T19:53:17Z | LLM Critics Help Catch LLM Bugs | Reinforcement learning from human feedback (RLHF) is fundamentally limited by the capacity of humans to correctly evaluate model output. To improve human evaluation ability and overcome that limitation this work trains "critic" models that help humans to more accurately evaluate model-written code. These critics are themselves LLMs trained with RLHF to write natural language feedback highlighting problems in code from real-world assistant tasks. On code containing naturally occurring LLM errors model-written critiques are preferred over human critiques in 63% of cases, and human evaluation finds that models catch more bugs than human contractors paid for code review. We further confirm that our fine-tuned LLM critics can successfully identify hundreds of errors in ChatGPT training data rated as "flawless", even though the majority of those tasks are non-code tasks and thus out-of-distribution for the critic model. Critics can have limitations of their own, including hallucinated bugs that could mislead humans into making mistakes they might have otherwise avoided, but human-machine teams of critics and contractors catch similar numbers of bugs to LLM critics while hallucinating less than LLMs alone. | [
"['Nat McAleese' 'Rai Michael Pokorny' 'Juan Felipe Ceron Uribe'\n 'Evgenia Nitishinskaya' 'Maja Trebacz' 'Jan Leike']"
] |
null | null | 2407.00233 | null | null | http://arxiv.org/pdf/2407.00233v1 | 2024-06-28T21:08:10Z | 2024-06-28T21:08:10Z | Methodology to Deploy CNN-Based Computer Vision Models on Immersive
Wearable Devices | Convolutional Neural Network (CNN) models often lack the ability to incorporate human input, which can be addressed by Augmented Reality (AR) headsets. However, current AR headsets face limitations in processing power, which has prevented researchers from performing real-time, complex image recognition tasks using CNNs in AR headsets. This paper presents a method to deploy CNN models on AR headsets by training them on computers and transferring the optimized weight matrices to the headset. The approach transforms the image data and CNN layers into a one-dimensional format suitable for the AR platform. We demonstrate this method by training the LeNet-5 CNN model on the MNIST dataset using PyTorch and deploying it on a HoloLens AR headset. The results show that the model maintains an accuracy of approximately 98%, similar to its performance on a computer. This integration of CNN and AR enables real-time image processing on AR headsets, allowing for the incorporation of human input into AI models. | [
"['Kaveh Malek' 'Fernando Moreu']"
] |
null | null | 2407.00236 | null | null | http://arxiv.org/pdf/2407.00236v1 | 2024-06-28T21:13:57Z | 2024-06-28T21:13:57Z | Closed-Form Test Functions for Biophysical Sequence Optimization
Algorithms | There is a growing body of work seeking to replicate the success of machine learning (ML) on domains like computer vision (CV) and natural language processing (NLP) to applications involving biophysical data. One of the key ingredients of prior successes in CV and NLP was the broad acceptance of difficult benchmarks that distilled key subproblems into approachable tasks that any junior researcher could investigate, but good benchmarks for biophysical domains are rare. This scarcity is partially due to a narrow focus on benchmarks which simulate biophysical data; we propose instead to carefully abstract biophysical problems into simpler ones with key geometric similarities. In particular we propose a new class of closed-form test functions for biophysical sequence optimization, which we call Ehrlich functions. We provide empirical results demonstrating these functions are interesting objects of study and can be non-trivial to solve with a standard genetic optimization baseline. | [
"['Samuel Stanton' 'Robert Alberstein' 'Nathan Frey' 'Andrew Watkins'\n 'Kyunghyun Cho']"
] |
null | null | 2407.00245 | null | null | http://arxiv.org/pdf/2407.00245v1 | 2024-06-28T22:04:36Z | 2024-06-28T22:04:36Z | Learning Closed Signal Flow Graphs | We develop a learning algorithm for closed signal flow graphs - a graphical model of signal transducers. The algorithm relies on the correspondence between closed signal flow graphs and weighted finite automata on a singleton alphabet. We demonstrate that this procedure results in a genuine reduction of complexity: our algorithm fares better than existing learning algorithms for weighted automata restricted to the case of a singleton alphabet. | [
"['Ekaterina Piotrovskaya' 'Leo Lobski' 'Fabio Zanasi']"
] |
null | null | 2407.00256 | null | null | http://arxiv.org/pdf/2407.00256v1 | 2024-06-28T23:05:08Z | 2024-06-28T23:05:08Z | One Prompt is not Enough: Automated Construction of a Mixture-of-Expert
Prompts | Large Language Models (LLMs) exhibit strong generalization capabilities to novel tasks when prompted with language instructions and in-context demos. Since this ability sensitively depends on the quality of prompts, various methods have been explored to automate the instruction design. While these methods demonstrated promising results, they also restricted the searched prompt to one instruction. Such simplification significantly limits their capacity, as a single demo-free instruction might not be able to cover the entire complex problem space of the targeted task. To alleviate this issue, we adopt the Mixture-of-Expert paradigm and divide the problem space into a set of sub-regions; Each sub-region is governed by a specialized expert, equipped with both an instruction and a set of demos. A two-phase process is developed to construct the specialized expert for each region: (1) demo assignment: Inspired by the theoretical connection between in-context learning and kernel regression, we group demos into experts based on their semantic similarity; (2) instruction assignment: A region-based joint search of an instruction per expert complements the demos assigned to it, yielding a synergistic effect. The resulting method, codenamed Mixture-of-Prompts (MoP), achieves an average win rate of 81% against prior arts across several major benchmarks. | [
"['Ruochen Wang' 'Sohyun An' 'Minhao Cheng' 'Tianyi Zhou' 'Sung Ju Hwang'\n 'Cho-Jui Hsieh']"
] |
null | null | 2407.00264 | null | null | http://arxiv.org/pdf/2407.00264v1 | 2024-06-28T23:31:22Z | 2024-06-28T23:31:22Z | External Model Motivated Agents: Reinforcement Learning for Enhanced
Environment Sampling | Unlike reinforcement learning (RL) agents, humans remain capable multitaskers in changing environments. In spite of only experiencing the world through their own observations and interactions, people know how to balance focusing on tasks with learning about how changes may affect their understanding of the world. This is possible by choosing to solve tasks in ways that are interesting and generally informative beyond just the current task. Motivated by this, we propose an agent influence framework for RL agents to improve the adaptation efficiency of external models in changing environments without any changes to the agent's rewards. Our formulation is composed of two self-contained modules: interest fields and behavior shaping via interest fields. We implement an uncertainty-based interest field algorithm as well as a skill-sampling-based behavior-shaping algorithm to use in testing this framework. Our results show that our method outperforms the baselines in terms of external model adaptation on metrics that measure both efficiency and performance. | [
"['Rishav Bhagat' 'Jonathan Balloch' 'Zhiyu Lin' 'Julia Kim' 'Mark Riedl']"
] |
null | null | 2407.00267 | null | null | http://arxiv.org/pdf/2407.00267v1 | 2024-06-29T00:44:33Z | 2024-06-29T00:44:33Z | Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection
in Breast Ultrasound | Detecting and classifying lesions in breast ultrasound images is a promising application of artificial intelligence (AI) for reducing the burden of cancer in regions with limited access to mammography. Such AI systems are more likely to be useful in a clinical setting if their predictions can be explained to a radiologist. This work proposes an explainable AI model that provides interpretable predictions using a standard lexicon from the American College of Radiology's Breast Imaging and Reporting Data System (BI-RADS). The model is a deep neural network featuring a concept bottleneck layer in which known BI-RADS features are predicted before making a final cancer classification. This enables radiologists to easily review the predictions of the AI system and potentially fix errors in real time by modifying the concept predictions. In experiments, a model is developed on 8,854 images from 994 women with expert annotations and histological cancer labels. The model outperforms state-of-the-art lesion detection frameworks with 48.9 average precision on the held-out testing set, and for cancer classification, concept intervention is shown to increase performance from 0.876 to 0.885 area under the receiver operating characteristic curve. Training and evaluation code is available at https://github.com/hawaii-ai/bus-cbm. | [
"['Arianna Bunnell' 'Yannik Glaser' 'Dustin Valdez' 'Thomas Wolfgruber'\n 'Aleen Altamirano' 'Carol Zamora González' 'Brenda Y. Hernandez'\n 'Peter Sadowski' 'John A. Shepherd']"
] |
null | null | 2407.00278 | null | null | http://arxiv.org/pdf/2407.00278v1 | 2024-06-29T02:06:01Z | 2024-06-29T02:06:01Z | PerAct2: A Perceiver Actor Framework for Bimanual Manipulation Tasks | Bimanual manipulation is challenging due to precise spatial and temporal coordination required between two arms. While there exist several real-world bimanual systems, there is a lack of simulated benchmarks with a large task diversity for systematically studying bimanual capabilities across a wide range of tabletop tasks. This paper addresses the gap by extending RLBench to bimanual manipulation. We open-source our code and benchmark comprising 13 new tasks with 23 unique task variations, each requiring a high degree of coordination and adaptability. To kickstart the benchmark, we extended several state-of-the art methods to bimanual manipulation and also present a language-conditioned behavioral cloning agent -- PerAct2, which enables the learning and execution of bimanual 6-DoF manipulation tasks. Our novel network architecture efficiently integrates language processing with action prediction, allowing robots to understand and perform complex bimanual tasks in response to user-specified goals. Project website with code is available at: http://bimanual.github.io | [
"['Markus Grotz' 'Mohit Shridhar' 'Tamim Asfour' 'Dieter Fox']"
] |
null | null | 2407.00286 | null | null | http://arxiv.org/pdf/2407.00286v1 | 2024-06-29T02:40:28Z | 2024-06-29T02:40:28Z | Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching
in Wireless Networks | Optimizing edge caching is crucial for the advancement of next-generation (nextG) wireless networks, ensuring high-speed and low-latency services for mobile users. Existing data-driven optimization approaches often lack awareness of the distribution of random data variables and focus solely on optimizing cache hit rates, neglecting potential reliability concerns, such as base station overload and unbalanced cache issues. This oversight can result in system crashes and degraded user experience. To bridge this gap, we introduce a novel digital twin-assisted optimization framework, called D-REC, which integrates reinforcement learning (RL) with diverse intervention modules to ensure reliable caching in nextG wireless networks. We first develop a joint vertical and horizontal twinning approach to efficiently create network digital twins, which are then employed by D-REC as RL optimizers and safeguards, providing ample datasets for training and predictive evaluation of our cache replacement policy. By incorporating reliability modules into a constrained Markov decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints, minimizing the risk of network failures. Theoretical analysis demonstrates comparable convergence rates between D-REC and vanilla data-driven methods without compromising caching performance. Extensive experiments validate that D-REC outperforms conventional approaches in cache hit rate and load balancing while effectively enforcing predetermined reliability intervention modules. | [
"['Zifan Zhang' 'Yuchen Liu' 'Zhiyuan Peng' 'Mingzhe Chen' 'Dongkuan Xu'\n 'Shuguang Cui']"
] |
null | null | 2407.00294 | null | null | http://arxiv.org/pdf/2407.00294v1 | 2024-06-29T03:25:54Z | 2024-06-29T03:25:54Z | Deep Neural Networks with Symplectic Preservation Properties | We propose a deep neural network architecture designed such that its output forms an invertible symplectomorphism of the input. This design draws an analogy to the real-valued non-volume-preserving (real NVP) method used in normalizing flow techniques. Utilizing this neural network type allows for learning tasks on unknown Hamiltonian systems without breaking the inherent symplectic structure of the phase space. | [
"['Qing He' 'Wei Cai']"
] |
null | null | 2407.00299 | null | null | http://arxiv.org/pdf/2407.00299v2 | 2024-07-02T11:15:11Z | 2024-06-29T03:37:29Z | Human-Agent Joint Learning for Efficient Robot Manipulation Skill
Acquisition | Employing a teleoperation system for gathering demonstrations offers the potential for more efficient learning of robot manipulation. However, teleoperating a robot arm equipped with a dexterous hand or gripper, via a teleoperation system poses significant challenges due to its high dimensionality, complex motions, and differences in physiological structure. In this study, we introduce a novel system for joint learning between human operators and robots, that enables human operators to share control of a robot end-effector with a learned assistive agent, facilitating simultaneous human demonstration collection and robot manipulation teaching. In this setup, as data accumulates, the assistive agent gradually learns. Consequently, less human effort and attention are required, enhancing the efficiency of the data collection process. It also allows the human operator to adjust the control ratio to achieve a trade-off between manual and automated control. We conducted experiments in both simulated environments and physical real-world settings. Through user studies and quantitative evaluations, it is evident that the proposed system could enhance data collection efficiency and reduce the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks. Videos are available at https://norweig1an.github.io/human-agent-joint-learning.github.io/. | [
"['Shengcheng Luo' 'Quanquan Peng' 'Jun Lv' 'Kaiwen Hong'\n 'Katherine Rose Driggs-Campbell' 'Cewu Lu' 'Yong-Lu Li']"
] |
null | null | 2407.00320 | null | null | http://arxiv.org/pdf/2407.00320v1 | 2024-06-29T05:14:04Z | 2024-06-29T05:14:04Z | LiteSearch: Efficacious Tree Search for LLM | Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree Search) can dramatically boost LLM performance on complex mathematical reasoning tasks. However, they often require more than 10 times the computational resources of greedy decoding due to wasteful search strategies, making them difficult to be deployed in practical applications. This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget (maximum number of children) calculation to tackle this issue. By considering the search progress towards the final answer (history) and the guidance from a value network (future) trained without any step-wise annotations, our algorithm iteratively selects the most promising tree node before expanding it within the boundaries of the allocated computational budget. Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach not only offers competitive performance but also enjoys significantly lower computational costs compared to baseline methods. | [
"['Ante Wang' 'Linfeng Song' 'Ye Tian' 'Baolin Peng' 'Dian Yu' 'Haitao Mi'\n 'Jinsong Su' 'Dong Yu']"
] |
null | null | 2407.00324 | null | null | http://arxiv.org/pdf/2407.00324v2 | 2024-07-08T20:15:46Z | 2024-06-29T05:55:33Z | Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning | Many real-world robot learning problems, such as pick-and-place or arriving at a destination, can be seen as a problem of reaching a goal state as soon as possible. These problems, when formulated as episodic reinforcement learning tasks, can easily be specified to align well with our intended goal: -1 reward every time step with termination upon reaching the goal state, called minimum-time tasks. Despite this simplicity, such formulations are often overlooked in favor of dense rewards due to their perceived difficulty and lack of informativeness. Our studies contrast the two reward paradigms, revealing that the minimum-time task specification not only facilitates learning higher-quality policies but can also surpass dense-reward-based policies on their own performance metrics. Crucially, we also identify the goal-hit rate of the initial policy as a robust early indicator for learning success in such sparse feedback settings. Finally, using four distinct real-robotic platforms, we show that it is possible to learn pixel-based policies from scratch within two to three hours using constant negative rewards. | [
"['Gautham Vasan' 'Yan Wang' 'Fahim Shahriar' 'James Bergstra'\n 'Martin Jagersand' 'A. Rupam Mahmood']"
] |
null | null | 2407.00332 | null | null | http://arxiv.org/pdf/2407.00332v1 | 2024-06-29T06:27:52Z | 2024-06-29T06:27:52Z | Machine Learning Models for Dengue Forecasting in Singapore | With emerging prevalence beyond traditionally endemic regions, the global burden of dengue disease is forecasted to be one of the fastest growing. With limited direct treatment or vaccination currently available, prevention through vector control is widely believed to be the most effective form of managing outbreaks. This study examines traditional state space models (moving average, autoregressive, ARIMA, SARIMA), supervised learning techniques (XGBoost, SVM, KNN) and deep networks (LSTM, CNN, ConvLSTM) for forecasting weekly dengue cases in Singapore. Meteorological data and search engine trends were included as features for ML techniques. Forecasts using CNNs yielded lowest RMSE in weekly cases in 2019. | [
"['Zi Iun Lai' 'Wai Kit Fung' 'Enquan Chew']"
] |
null | null | 2407.00336 | null | null | http://arxiv.org/pdf/2407.00336v1 | 2024-06-29T06:47:51Z | 2024-06-29T06:47:51Z | Dual-view Aware Smart Contract Vulnerability Detection for Ethereum | The wide application of Ethereum technology has brought technological innovation to traditional industries. As one of Ethereum's core applications, smart contracts utilize diverse contract codes to meet various functional needs and have gained widespread use. However, the non-tamperability of smart contracts, coupled with vulnerabilities caused by natural flaws or human errors, has brought unprecedented challenges to blockchain security. Therefore, in order to ensure the healthy development of blockchain technology and the stability of the blockchain community, it is particularly important to study the vulnerability detection techniques for smart contracts. In this paper, we propose a Dual-view Aware Smart Contract Vulnerability Detection Framework named DVDet. The framework initially converts the source code and bytecode of smart contracts into weighted graphs and control flow sequences, capturing potential risk features from these two perspectives and integrating them for analysis, ultimately achieving effective contract vulnerability detection. Comprehensive experiments on the Ethereum dataset show that our method outperforms others in detecting vulnerabilities. | [
"['Jiacheng Yao' 'Maolin Wang' 'Wanqi Chen' 'Chengxiang Jin' 'Jiajun Zhou'\n 'Shanqing Yu' 'Qi Xuan']"
] |
null | null | 2407.00337 | null | null | http://arxiv.org/pdf/2407.00337v1 | 2024-06-29T06:52:59Z | 2024-06-29T06:52:59Z | WgLaSDI: Weak-Form Greedy Latent Space Dynamics Identification | The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. To enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared to the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5-10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1-7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1,779x speed-up. | [
"['Xiaolong He' 'April Tran' 'David M. Bortz' 'Youngsoo Choi']"
] |
null | null | 2407.00356 | null | null | http://arxiv.org/pdf/2407.00356v1 | 2024-06-29T08:07:39Z | 2024-06-29T08:07:39Z | Enhancing Accuracy and Parameter-Efficiency of Neural Representations
for Network Parameterization | In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks. We present a surprising finding that, when recovering the original model accuracy is the sole objective, it can be achieved effectively through the weight reconstruction objective alone. Additionally, we explore the underlying factors for improving weight reconstruction under parameter-efficiency constraints, and propose a novel training scheme that decouples the reconstruction objective from auxiliary objectives such as knowledge distillation that leads to significant improvements compared to state-of-the-art approaches. Finally, these results pave way for more practical scenarios, where one needs to achieve improvements on both model accuracy and predictor network parameter-efficiency simultaneously. | [
"['Hongjun Choi' 'Jayaraman J. Thiagarajan' 'Ruben Glatt' 'Shusen Liu']"
] |
null | null | 2407.00371 | null | null | http://arxiv.org/pdf/2407.00371v1 | 2024-06-29T08:43:38Z | 2024-06-29T08:43:38Z | Axiomatization of Gradient Smoothing in Neural Networks | Gradients play a pivotal role in neural networks explanation. The inherent high dimensionality and structural complexity of neural networks result in the original gradients containing a significant amount of noise. While several approaches were proposed to reduce noise with smoothing, there is little discussion of the rationale behind smoothing gradients in neural networks. In this work, we proposed a gradient smooth theoretical framework for neural networks based on the function mollification and Monte Carlo integration. The framework intrinsically axiomatized gradient smoothing and reveals the rationale of existing methods. Furthermore, we provided an approach to design new smooth methods derived from the framework. By experimental measurement of several newly designed smooth methods, we demonstrated the research potential of our framework. | [
"['Linjiang Zhou' 'Xiaochuan Shi' 'Chao Ma' 'Zepeng Wang']"
] |
null | null | 2407.00382 | null | null | http://arxiv.org/pdf/2407.00382v2 | 2024-07-02T03:22:04Z | 2024-06-29T09:35:12Z | Towards Universal Mesh Movement Networks | Solving complex Partial Differential Equations (PDEs) accurately and efficiently is an essential and challenging problem in all scientific and engineering disciplines. Mesh movement methods provide the capability to improve the accuracy of the numerical solution without increasing the overall mesh degree of freedom count. Conventional sophisticated mesh movement methods are extremely expensive and struggle to handle scenarios with complex boundary geometries. However, existing learning-based methods require re-training from scratch given a different PDE type or boundary geometry, which limits their applicability, and also often suffer from robustness issues in the form of inverted elements. In this paper, we introduce the Universal Mesh Movement Network (UM2N), which -- once trained -- can be applied in a non-intrusive, zero-shot manner to move meshes with different size distributions and structures, for solvers applicable to different PDE types and boundary geometries. UM2N consists of a Graph Transformer (GT) encoder for extracting features and a Graph Attention Network (GAT) based decoder for moving the mesh. We evaluate our method on advection and Navier-Stokes based examples, as well as a real-world tsunami simulation case. Our method outperforms existing learning-based mesh movement methods in terms of the benchmarks described above. In comparison to the conventional sophisticated Monge-Amp`ere PDE-solver based method, our approach not only significantly accelerates mesh movement, but also proves effective in scenarios where the conventional method fails. Our project page is at https://erizmr.github.io/UM2N/. | [
"['Mingrui Zhang' 'Chunyang Wang' 'Stephan Kramer' 'Joseph G. Wallwork'\n 'Siyi Li' 'Jiancheng Liu' 'Xiang Chen' 'Matthew D. Piggott']"
] |
null | null | 2407.00383 | null | null | http://arxiv.org/pdf/2407.00383v1 | 2024-06-29T09:49:16Z | 2024-06-29T09:49:16Z | FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for
Unsupervised Graph-Level Anomaly Detection | Unsupervised graph-level anomaly detection (UGAD) has attracted increasing interest due to its widespread application. In recent studies, knowledge distillation-based methods have been widely used in unsupervised anomaly detection to improve model efficiency and generalization. However, the inherent symmetry between the source (teacher) and target (student) networks typically results in consistent outputs across both architectures, making it difficult to distinguish abnormal graphs from normal graphs. Also, existing methods mainly rely on graph features to distinguish anomalies, which may be unstable with complex and diverse data and fail to capture the essence that differentiates normal graphs from abnormal ones. In this work, we propose a Graph Normalizing Flows-driven Asymmetric Network For Unsupervised Graph-Level Anomaly Detection (FANFOLD in short). We introduce normalizing flows to unsupervised graph-level anomaly detection due to their successful application and superior quality in learning the underlying distribution of samples. Specifically, we adopt the knowledge distillation technique and apply normalizing flows on the source network, achieving the asymmetric network. In the training stage, FANFOLD transforms the original distribution of normal graphs to a standard normal distribution. During inference, FANFOLD computes the anomaly score using the source-target loss to discriminate between normal and anomalous graphs. We conduct extensive experiments on 15 datasets of different fields with 9 baseline methods to validate the superiority of FANFOLD. | [
"['Rui Cao' 'Shijie Xue' 'Jindong Li' 'Qi Wang' 'Yi Chang']"
] |
null | null | 2407.00388 | null | null | http://arxiv.org/pdf/2407.00388v1 | 2024-06-29T10:08:23Z | 2024-06-29T10:08:23Z | Weighted mesh algorithms for general Markov decision processes:
Convergence and tractability | We introduce a mesh-type approach for tackling discrete-time, finite-horizon Markov Decision Processes (MDPs) characterized by state and action spaces that are general, encompassing both finite and infinite (yet suitably regular) subsets of Euclidean space. In particular, for bounded state and action spaces, our algorithm achieves a computational complexity that is tractable in the sense of Novak and Wozniakowski, and is polynomial in the time horizon. For unbounded state space the algorithm is "semi-tractable" in the sense that the complexity is proportional to $epsilon^{-c}$ with some dimension independent $cgeq2$, for achieving an accuracy $epsilon$, and polynomial in the time horizon with degree linear in the underlying dimension. As such the proposed approach has some flavor of the randomization method by Rust which deals with infinite horizon MDPs and uniform sampling in compact state space. However, the present approach is essentially different due to the finite horizon and a simulation procedure due to general transition distributions, and more general in the sense that it encompasses unbounded state space. To demonstrate the effectiveness of our algorithm, we provide illustrations based on Linear-Quadratic Gaussian (LQG) control problems. | [
"['Denis Belomestny' 'John Schoenmakers']"
] |
null | null | 2407.00397 | null | null | http://arxiv.org/pdf/2407.00397v1 | 2024-06-29T10:50:23Z | 2024-06-29T10:50:23Z | Markovian Gaussian Process: A Universal State-Space Representation for
Stationary Temporal Gaussian Process | Gaussian Processes (GPs) and Linear Dynamical Systems (LDSs) are essential time series and dynamic system modeling tools. GPs can handle complex, nonlinear dynamics but are computationally demanding, while LDSs offer efficient computation but lack the expressive power of GPs. To combine their benefits, we introduce a universal method that allows an LDS to mirror stationary temporal GPs. This state-space representation, known as the Markovian Gaussian Process (Markovian GP), leverages the flexibility of kernel functions while maintaining efficient linear computation. Unlike existing GP-LDS conversion methods, which require separability for most multi-output kernels, our approach works universally for single- and multi-output stationary temporal kernels. We evaluate our method by computing covariance, performing regression tasks, and applying it to a neuroscience application, demonstrating that our method provides an accurate state-space representation for stationary temporal GPs. | [
"['Weihan Li' 'Yule Wang' 'Chengrui Li' 'Anqi Wu']"
] |
null | null | 2407.00401 | null | null | http://arxiv.org/pdf/2407.00401v1 | 2024-06-29T11:02:05Z | 2024-06-29T11:02:05Z | PUZZLES: A Benchmark for Neural Algorithmic Reasoning | Algorithmic reasoning is a fundamental cognitive ability that plays a pivotal role in problem-solving and decision-making processes. Reinforcement Learning (RL) has demonstrated remarkable proficiency in tasks such as motor control, handling perceptual input, and managing stochastic environments. These advancements have been enabled in part by the availability of benchmarks. In this work we introduce PUZZLES, a benchmark based on Simon Tatham's Portable Puzzle Collection, aimed at fostering progress in algorithmic and logical reasoning in RL. PUZZLES contains 40 diverse logic puzzles of adjustable sizes and varying levels of complexity; many puzzles also feature a diverse set of additional configuration parameters. The 40 puzzles provide detailed information on the strengths and generalization capabilities of RL agents. Furthermore, we evaluate various RL algorithms on PUZZLES, providing baseline comparisons and demonstrating the potential for future research. All the software, including the environment, is available at https://github.com/ETH-DISCO/rlp. | [
"['Benjamin Estermann' 'Luca A. Lanzendörfer' 'Yannick Niedermayr'\n 'Roger Wattenhofer']"
] |
null | null | 2407.00411 | null | null | http://arxiv.org/pdf/2407.00411v1 | 2024-06-29T11:31:09Z | 2024-06-29T11:31:09Z | Explainability of Machine Learning Models under Missing Data | Missing data is a prevalent issue that can significantly impair model performance and interpretability. This paper briefly summarizes the development of the field of missing data with respect to Explainable Artificial Intelligence and experimentally investigates the effects of various imputation methods on the calculation of Shapley values, a popular technique for interpreting complex machine learning models. We compare different imputation strategies and assess their impact on feature importance and interaction as determined by Shapley values. Moreover, we also theoretically analyze the effects of missing values on Shapley values. Importantly, our findings reveal that the choice of imputation method can introduce biases that could lead to changes in the Shapley values, thereby affecting the interpretability of the model. Moreover, and that a lower test prediction mean square error (MSE) may not imply a lower MSE in Shapley values and vice versa. Also, while Xgboost is a method that could handle missing data directly, using Xgboost directly on missing data can seriously affect interpretability compared to imputing the data before training Xgboost. This study provides a comprehensive evaluation of imputation methods in the context of model interpretation, offering practical guidance for selecting appropriate techniques based on dataset characteristics and analysis objectives. The results underscore the importance of considering imputation effects to ensure robust and reliable insights from machine learning models. | [
"['Tuan L. Vo' 'Thu Nguyen' 'Hugo L. Hammer' 'Michael A. Riegler'\n 'Pal Halvorsen']"
] |
null | null | 2407.00418 | null | null | http://arxiv.org/pdf/2407.00418v1 | 2024-06-29T11:59:20Z | 2024-06-29T11:59:20Z | eFontes. Part of Speech Tagging and Lemmatization of Medieval Latin
Texts.A Cross-Genre Survey | This study introduces the eFontes models for automatic linguistic annotation of Medieval Latin texts, focusing on lemmatization, part-of-speech tagging, and morphological feature determination. Using the Transformers library, these models were trained on Universal Dependencies (UD) corpora and the newly developed eFontes corpus of Polish Medieval Latin. The research evaluates the models' performance, addressing challenges such as orthographic variations and the integration of Latinized vernacular terms. The models achieved high accuracy rates: lemmatization at 92.60%, part-of-speech tagging at 83.29%, and morphological feature determination at 88.57%. The findings underscore the importance of high-quality annotated corpora and propose future enhancements, including extending the models to Named Entity Recognition. | [
"['Krzysztof Nowak' 'Jędrzej Ziębura' 'Krzysztof Wróbel'\n 'Aleksander Smywiński-Pohl']"
] |
null | null | 2407.00419 | null | null | http://arxiv.org/pdf/2407.00419v1 | 2024-06-29T11:59:52Z | 2024-06-29T11:59:52Z | On the Complexity of Learning to Cooperate with Populations of Socially
Rational Agents | Artificially intelligent agents deployed in the real-world will require the ability to reliably textit{cooperate} with humans (as well as other, heterogeneous AI agents). To provide formal guarantees of successful cooperation, we must make some assumptions about how partner agents could plausibly behave. Any realistic set of assumptions must account for the fact that other agents may be just as adaptable as our agent is. In this work, we consider the problem of cooperating with a textit{population} of agents in a finitely-repeated, two player general-sum matrix game with private utilities. Two natural assumptions in such settings are that: 1) all agents in the population are individually rational learners, and 2) when any two members of the population are paired together, with high-probability they will achieve at least the same utility as they would under some Pareto efficient equilibrium strategy. Our results first show that these assumptions alone are insufficient to ensure textit{zero-shot} cooperation with members of the target population. We therefore consider the problem of textit{learning} a strategy for cooperating with such a population using prior observations its members interacting with one another. We provide upper and lower bounds on the number of samples needed to learn an effective cooperation strategy. Most importantly, we show that these bounds can be much stronger than those arising from a "naive'' reduction of the problem to one of imitation learning. | [
"['Robert Loftin' 'Saptarashmi Bandyopadhyay' 'Mustafa Mert Çelikok']"
] |
null | null | 2407.00429 | null | null | http://arxiv.org/pdf/2407.00429v1 | 2024-06-29T12:48:53Z | 2024-06-29T12:48:53Z | Time Series Clustering with General State Space Models via Stochastic
Variational Inference | In this paper, we propose a novel method of model-based time series clustering with mixtures of general state space models (MSSMs). Each component of MSSMs is associated with each cluster. An advantage of the proposed method is that it enables the use of time series models appropriate to the specific time series. This not only improves clustering and prediction accuracy but also enhances the interpretability of the estimated parameters. The parameters of the MSSMs are estimated using stochastic variational inference, a subtype of variational inference. The proposed method estimates the latent variables of an arbitrary state space model by using neural networks with a normalizing flow as a variational estimator. The number of clusters can be estimated using the Bayesian information criterion. In addition, to prevent MSSMs from converging to the local optimum, we propose several optimization tricks, including an additional penalty term called entropy annealing. Experiments on simulated datasets show that the proposed method is effective for clustering, parameter estimation, and estimating the number of clusters. | [
"['Ryoichi Ishizuka' 'Takashi Imai' 'Kaoru Kawamoto']"
] |
null | null | 2407.00449 | null | null | http://arxiv.org/pdf/2407.00449v1 | 2024-06-29T14:19:40Z | 2024-06-29T14:19:40Z | Fully tensorial approach to hypercomplex neural networks | Fully tensorial theory of hypercomplex neural networks is given. The key point is to observe that the algebra multiplication can be represented as a rank three tensor. This approach is attractive for neural network libraries that support effective tensorial operations. | [
"['Agnieszka Niemczynowicz' 'Radosław Antoni Kycia']"
] |
null | null | 2407.00452 | null | null | http://arxiv.org/pdf/2407.00452v1 | 2024-06-29T14:36:37Z | 2024-06-29T14:36:37Z | KHNNs: hypercomplex neural networks computations via Keras using
TensorFlow and PyTorch | Neural networks used in computations with more advanced algebras than real numbers perform better in some applications. However, there is no general framework for constructing hypercomplex neural networks. We propose a library integrated with Keras that can do computations within TensorFlow and PyTorch. It provides Dense and Convolutional 1D, 2D, and 3D layers architectures. | [
"['Agnieszka Niemczynowicz' 'Radosław Antoni Kycia']"
] |
null | null | 2407.00453 | null | null | http://arxiv.org/pdf/2407.00453v1 | 2024-06-29T14:37:36Z | 2024-06-29T14:37:36Z | PerSEval: Assessing Personalization in Text Summarizers | Personalized summarization models cater to individuals' subjective understanding of saliency, as represented by their reading history and current topics of attention. Existing personalized text summarizers are primarily evaluated based on accuracy measures such as BLEU, ROUGE, and METEOR. However, a recent study argued that accuracy measures are inadequate for evaluating the degree of personalization of these models and proposed EGISES, the first metric to evaluate personalized text summaries. It was suggested that accuracy is a separate aspect and should be evaluated standalone. In this paper, we challenge the necessity of an accuracy leaderboard, suggesting that relying on accuracy-based aggregated results might lead to misleading conclusions. To support this, we delve deeper into EGISES, demonstrating both theoretically and empirically that it measures the degree of responsiveness, a necessary but not sufficient condition for degree-of-personalization. We subsequently propose PerSEval, a novel measure that satisfies the required sufficiency condition. Based on the benchmarking of ten SOTA summarization models on the PENS dataset, we empirically establish that -- (i) PerSEval is reliable w.r.t human-judgment correlation (Pearson's r = 0.73; Spearman's $rho$ = 0.62; Kendall's $tau$ = 0.42), (ii) PerSEval has high rank-stability, (iii) PerSEval as a rank-measure is not entailed by EGISES-based ranking, and (iv) PerSEval can be a standalone rank-measure without the need of any aggregated ranking. | [
"['Sourish Dasgupta' 'Ankush Chander' 'Parth Borad' 'Isha Motiyani'\n 'Tanmoy Chakraborty']"
] |
null | null | 2407.00463 | null | null | http://arxiv.org/pdf/2407.00463v3 | 2024-07-15T12:56:28Z | 2024-06-29T15:20:11Z | Open-Source Conversational AI with SpeechBrain 1.0 | SpeechBrain is an open-source Conversational AI toolkit based on PyTorch, focused particularly on speech processing tasks such as speech recognition, speech enhancement, speaker recognition, text-to-speech, and much more. It promotes transparency and replicability by releasing both the pre-trained models and the complete "recipes" of code and algorithms required for training them. This paper presents SpeechBrain 1.0, a significant milestone in the evolution of the toolkit, which now has over 200 recipes for speech, audio, and language processing tasks, and more than 100 models available on Hugging Face. SpeechBrain 1.0 introduces new technologies to support diverse learning modalities, Large Language Model (LLM) integration, and advanced decoding strategies, along with novel models, tasks, and modalities. It also includes a new benchmark repository, offering researchers a unified platform for evaluating models across diverse tasks | [
"['Mirco Ravanelli' 'Titouan Parcollet' 'Adel Moumen' 'Sylvain de Langen'\n 'Cem Subakan' 'Peter Plantinga' 'Yingzhi Wang' 'Pooneh Mousavi'\n 'Luca Della Libera' 'Artem Ploujnikov' 'Francesco Paissan' 'Davide Borra'\n 'Salah Zaiem' 'Zeyu Zhao' 'Shucong Zhang' 'Georgios Karakasidis'\n 'Sung-Lin Yeh' 'Aku Rouhe' 'Rudolf Braun' 'Florian Mai'\n 'Juan Zuluaga-Gomez' 'Seyed Mahed Mousavi' 'Andreas Nautsch'\n 'Xuechen Liu' 'Sangeet Sagar' 'Jarod Duret' 'Salima Mdhaffar'\n 'Gaelle Laperriere' 'Renato De Mori' 'Yannick Esteve']"
] |
null | null | 2407.00465 | null | null | http://arxiv.org/pdf/2407.00465v1 | 2024-06-29T15:21:20Z | 2024-06-29T15:21:20Z | Characterizing Continual Learning Scenarios and Strategies for Audio
Analysis | Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. In this paper, we characterize continual learning (CL) approaches in audio analysis. In this paper, we characterize continual learning (CL) approaches, intended to tackle catastrophic forgetting arising due to drifts. As there is no CL dataset for audio analysis, we use DCASE 2020 to 2023 datasets to create various CL scenarios for audio-based monitoring tasks. We have investigated the following CL and non-CL approaches: EWC, LwF, SI, GEM, A-GEM, GDumb, Replay, Naive, cumulative, and joint training. The study is very beneficial for researchers and practitioners working in the area of audio analysis for developing adaptive models. We observed that Replay achieved better results than other methods in the DCASE challenge data. It achieved an accuracy of 70.12% for the domain incremental scenario and an accuracy of 96.98% for the class incremental scenario. | [
"['Ruchi Bhatt' 'Pratibha Kumari' 'Dwarikanath Mahapatra'\n 'Abdulmotaleb El Saddik' 'Mukesh Saini']"
] |
null | null | 2407.00467 | null | null | http://arxiv.org/pdf/2407.00467v1 | 2024-06-29T15:24:33Z | 2024-06-29T15:24:33Z | VcLLM: Video Codecs are Secretly Tensor Codecs | As the parameter size of large language models (LLMs) continues to expand, the need for a large memory footprint and high communication bandwidth have become significant bottlenecks for the training and inference of LLMs. To mitigate these bottlenecks, various tensor compression techniques have been proposed to reduce the data size, thereby alleviating memory requirements and communication pressure. Our research found that video codecs, despite being originally designed for compressing videos, show excellent efficiency when compressing various types of tensors. We demonstrate that video codecs can be versatile and general-purpose tensor codecs while achieving the state-of-the-art compression efficiency in various tasks. We further make use of the hardware video encoding and decoding module available on GPUs to create a framework capable of both inference and training with video codecs repurposed as tensor codecs. This greatly reduces the requirement for memory capacity and communication bandwidth, enabling training and inference of large models on consumer-grade GPUs. | [
"['Ceyu Xu' 'Yongji Wu' 'Xinyu Yang' 'Beidi Chen' 'Matthew Lentz'\n 'Danyang Zhuo' 'Lisa Wu Wills']"
] |
null | null | 2407.00474 | null | null | http://arxiv.org/pdf/2407.00474v1 | 2024-06-29T15:38:37Z | 2024-06-29T15:38:37Z | MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global
Bypass for Medical Image Analysis | In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy. Federated learning facilitates collaborative model development without the need to share local data from healthcare institutions. Yet, the statistical and system heterogeneity among these institutions poses substantial challenges, which affects the effectiveness of federated learning and hampers the exchange of information between clients. To address these issues, we introduce a novel approach, MH-pFLGB, which employs a global bypass strategy to mitigate the reliance on public datasets and navigate the complexities of non-IID data distributions. Our method enhances traditional federated learning by integrating a global bypass model, which would share the information among the clients, but also serves as part of the network to enhance the performance on each client. Additionally, MH-pFLGB provides a feature fusion module to better combine the local and global features. We validate model{}'s effectiveness and adaptability through extensive testing on different medical tasks, demonstrating superior performance compared to existing state-of-the-art methods. | [
"['Luyuan Xie' 'Manqing Lin' 'ChenMing Xu' 'Tianyu Luan' 'Zhipeng Zeng'\n 'Wenjun Qian' 'Cong Li' 'Yuejian Fang' 'Qingni Shen' 'Zhonghai Wu']"
] |
null | null | 2407.00478 | null | null | http://arxiv.org/pdf/2407.00478v1 | 2024-06-29T15:52:37Z | 2024-06-29T15:52:37Z | Knowledge-Aware Parsimony Learning: A Perspective from Relational Graphs | The scaling law, a strategy that involves the brute-force scaling of the training dataset and learnable parameters, has become a prevalent approach for developing stronger learning models. In this paper, we examine its rationale in terms of learning from relational graphs. We demonstrate that directly adhering to such a scaling law does not necessarily yield stronger models due to architectural incompatibility and representation bottlenecks. To tackle this challenge, we propose a novel framework for learning from relational graphs via knowledge-aware parsimony learning. Our method draws inspiration from the duality between data and knowledge inherent in these graphs. Specifically, we first extract knowledge (like symbolic logic and physical laws) during the learning process, and then apply combinatorial generalization to the task at hand. This extracted knowledge serves as the ``building blocks'' for achieving parsimony learning. By applying this philosophy to architecture, parameters, and inference, we can effectively achieve versatile, sample-efficient, and interpretable learning. Experimental results show that our proposed framework surpasses methods that strictly follow the traditional scaling-up roadmap. This highlights the importance of incorporating knowledge in the development of next-generation learning technologies. | [
"['Quanming Yao' 'Yongqi Zhang' 'Yaqing Wang' 'Nan Yin' 'James Kwok'\n 'Qiang Yang']"
] |
null | null | 2407.00482 | null | null | http://arxiv.org/pdf/2407.00482v1 | 2024-06-29T16:05:47Z | 2024-06-29T16:05:47Z | Quantifying Spuriousness of Biased Datasets Using Partial Information
Decomposition | Spurious patterns refer to a mathematical association between two or more variables in a dataset that are not causally related. However, this notion of spuriousness, which is usually introduced due to sampling biases in the dataset, has classically lacked a formal definition. To address this gap, this work presents the first information-theoretic formalization of spuriousness in a dataset (given a split of spurious and core features) using a mathematical framework called Partial Information Decomposition (PID). Specifically, we disentangle the joint information content that the spurious and core features share about another target variable (e.g., the prediction label) into distinct components, namely unique, redundant, and synergistic information. We propose the use of unique information, with roots in Blackwell Sufficiency, as a novel metric to formally quantify dataset spuriousness and derive its desirable properties. We empirically demonstrate how higher unique information in the spurious features in a dataset could lead a model into choosing the spurious features over the core features for inference, often having low worst-group-accuracy. We also propose a novel autoencoder-based estimator for computing unique information that is able to handle high-dimensional image data. Finally, we also show how this unique information in the spurious feature is reduced across several dataset-based spurious-pattern-mitigation techniques such as data reweighting and varying levels of background mixing, demonstrating a novel tradeoff between unique information (spuriousness) and worst-group-accuracy. | [
"['Barproda Halder' 'Faisal Hamman' 'Pasan Dissanayake' 'Qiuyi Zhang'\n 'Ilia Sucholutsky' 'Sanghamitra Dutta']"
] |
null | null | 2407.00490 | null | null | http://arxiv.org/pdf/2407.00490v1 | 2024-06-29T16:44:29Z | 2024-06-29T16:44:29Z | Toward Global Convergence of Gradient EM for Over-Parameterized Gaussian
Mixture Models | We study the gradient Expectation-Maximization (EM) algorithm for Gaussian Mixture Models (GMM) in the over-parameterized setting, where a general GMM with $n>1$ components learns from data that are generated by a single ground truth Gaussian distribution. While results for the special case of 2-Gaussian mixtures are well-known, a general global convergence analysis for arbitrary $n$ remains unresolved and faces several new technical barriers since the convergence becomes sub-linear and non-monotonic. To address these challenges, we construct a novel likelihood-based convergence analysis framework and rigorously prove that gradient EM converges globally with a sublinear rate $O(1/sqrt{t})$. This is the first global convergence result for Gaussian mixtures with more than $2$ components. The sublinear convergence rate is due to the algorithmic nature of learning over-parameterized GMM with gradient EM. We also identify a new emerging technical challenge for learning general over-parameterized GMM: the existence of bad local regions that can trap gradient EM for an exponential number of steps. | [
"['Weihang Xu' 'Maryam Fazel' 'Simon S. Du']"
] |
null | null | 2407.00492 | null | null | http://arxiv.org/pdf/2407.00492v1 | 2024-06-29T16:49:28Z | 2024-06-29T16:49:28Z | Fast Gibbs sampling for the local and global trend Bayesian exponential
smoothing model | In Smyl et al. [Local and global trend Bayesian exponential smoothing models. International Journal of Forecasting, 2024.], a generalised exponential smoothing model was proposed that is able to capture strong trends and volatility in time series. This method achieved state-of-the-art performance in many forecasting tasks, but its fitting procedure, which is based on the NUTS sampler, is very computationally expensive. In this work, we propose several modifications to the original model, as well as a bespoke Gibbs sampler for posterior exploration; these changes improve sampling time by an order of magnitude, thus rendering the model much more practically relevant. The new model, and sampler, are evaluated on the M3 dataset and are shown to be competitive, or superior, in terms of accuracy to the original method, while being substantially faster to run. | [
"['Xueying Long' 'Daniel F. Schmidt' 'Christoph Bergmeir' 'Slawek Smyl']"
] |
null | null | 2407.00494 | null | null | http://arxiv.org/pdf/2407.00494v1 | 2024-06-29T17:11:09Z | 2024-06-29T17:11:09Z | Graph Neural Networks Gone Hogwild | Message passing graph neural networks (GNNs) would appear to be powerful tools to learn distributed algorithms via gradient descent, but generate catastrophically incorrect predictions when nodes update asynchronously during inference. This failure under asynchrony effectively excludes these architectures from many potential applications, such as learning local communication policies between resource-constrained agents in, e.g., robotic swarms or sensor networks. In this work we explore why this failure occurs in common GNN architectures, and identify "implicitly-defined" GNNs as a class of architectures which is provably robust to partially asynchronous "hogwild" inference, adapting convergence guarantees from work in asynchronous and distributed optimization, e.g., Bertsekas (1982); Niu et al. (2011). We then propose a novel implicitly-defined GNN architecture, which we call an energy GNN. We show that this architecture outperforms other GNNs from this class on a variety of synthetic tasks inspired by multi-agent systems, and achieves competitive performance on real-world datasets. | [
"['Olga Solodova' 'Nick Richardson' 'Deniz Oktay' 'Ryan P. Adams']"
] |
null | null | 2407.00495 | null | null | http://arxiv.org/pdf/2407.00495v1 | 2024-06-29T17:13:37Z | 2024-06-29T17:13:37Z | A Bayesian Solution To The Imitation Gap | In many real-world settings, an agent must learn to act in environments where no reward signal can be specified, but a set of expert demonstrations is available. Imitation learning (IL) is a popular framework for learning policies from such demonstrations. However, in some cases, differences in observability between the expert and the agent can give rise to an imitation gap such that the expert's policy is not optimal for the agent and a naive application of IL can fail catastrophically. In particular, if the expert observes the Markov state and the agent does not, then the expert will not demonstrate the information-gathering behavior needed by the agent but not the expert. In this paper, we propose a Bayesian solution to the Imitation Gap (BIG), first using the expert demonstrations, together with a prior specifying the cost of exploratory behavior that is not demonstrated, to infer a posterior over rewards with Bayesian inverse reinforcement learning (IRL). BIG then uses the reward posterior to learn a Bayes-optimal policy. Our experiments show that BIG, unlike IL, allows the agent to explore at test time when presented with an imitation gap, whilst still learning to behave optimally using expert demonstrations when no such gap exists. | [
"['Risto Vuorio' 'Mattie Fellows' 'Cong Lu' 'Clémence Grislain'\n 'Shimon Whiteson']"
] |
null | null | 2407.00496 | null | null | http://arxiv.org/pdf/2407.00496v1 | 2024-06-29T17:13:44Z | 2024-06-29T17:13:44Z | A Two-stage Reinforcement Learning-based Approach for Multi-entity Task
Allocation | Task allocation is a key combinatorial optimization problem, crucial for modern applications such as multi-robot cooperation and resource scheduling. Decision makers must allocate entities to tasks reasonably across different scenarios. However, traditional methods assume static attributes and numbers of tasks and entities, often relying on dynamic programming and heuristic algorithms for solutions. In reality, task allocation resembles Markov decision processes, with dynamically changing task and entity attributes. Thus, algorithms must dynamically allocate tasks based on their states. To address this issue, we propose a two-stage task allocation algorithm based on similarity, utilizing reinforcement learning to learn allocation strategies. The proposed pre-assign strategy allows entities to preselect appropriate tasks, effectively avoiding local optima and thereby better finding the optimal allocation. We also introduce an attention mechanism and a hyperparameter network structure to adapt to the changing number and attributes of entities and tasks, enabling our network structure to generalize to new tasks. Experimental results across multiple environments demonstrate that our algorithm effectively addresses the challenges of dynamic task allocation in practical applications. Compared to heuristic algorithms like genetic algorithms, our reinforcement learning approach better solves dynamic allocation problems and achieves zero-shot generalization to new tasks with good performance. The code is available at https://github.com/yk7333/TaskAllocation. | [
"['Aicheng Gong' 'Kai Yang' 'Jiafei Lyu' 'Xiu Li']"
] |
null | null | 2407.00499 | null | null | http://arxiv.org/pdf/2407.00499v1 | 2024-06-29T17:33:07Z | 2024-06-29T17:33:07Z | ConU: Conformal Uncertainty in Large Language Models with Correctness
Coverage Guarantees | Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the intricate nature of the recent large language models (LLMs). This study investigates adapting conformal prediction (CP), which can convert any heuristic measure of uncertainty into rigorous theoretical guarantees by constructing prediction sets, for black-box LLMs in open-ended NLG tasks. We propose a sampling-based uncertainty measure leveraging self-consistency and develop a conformal uncertainty criterion by integrating the uncertainty condition aligned with correctness into the design of the CP algorithm. Experimental results indicate that our uncertainty measure generally surpasses prior state-of-the-art methods. Furthermore, we calibrate the prediction sets within the model's unfixed answer distribution and achieve strict control over the correctness coverage rate across 6 LLMs on 4 free-form NLG datasets, spanning general-purpose and medical domains, while the small average set size further highlights the efficiency of our method in providing trustworthy guarantees for practical open-ended NLG applications. | [
"['Zhiyuan Wang' 'Jinhao Duan' 'Lu Cheng' 'Yue Zhang' 'Qingni Wang'\n 'Hengtao Shen' 'Xiaofeng Zhu' 'Xiaoshuang Shi' 'Kaidi Xu']"
] |
null | null | 2407.00500 | null | null | http://arxiv.org/pdf/2407.00500v1 | 2024-06-29T17:46:10Z | 2024-06-29T17:46:10Z | Intrinsic PAPR for Point-level 3D Scene Albedo and Shading Editing | Recent advancements in neural rendering have excelled at novel view synthesis from multi-view RGB images. However, they often lack the capability to edit the shading or colour of the scene at a detailed point-level, while ensuring consistency across different viewpoints. In this work, we address the challenge of point-level 3D scene albedo and shading editing from multi-view RGB images, focusing on detailed editing at the point-level rather than at a part or global level. While prior works based on volumetric representation such as NeRF struggle with achieving 3D consistent editing at the point level, recent advancements in point-based neural rendering show promise in overcoming this challenge. We introduce ``Intrinsic PAPR'', a novel method based on the recent point-based neural rendering technique Proximity Attention Point Rendering (PAPR). Unlike other point-based methods that model the intrinsic decomposition of the scene, our approach does not rely on complicated shading models or simplistic priors that may not universally apply. Instead, we directly model scene decomposition into albedo and shading components, leading to better estimation accuracy. Comparative evaluations against the latest point-based inverse rendering methods demonstrate that Intrinsic PAPR achieves higher-quality novel view rendering and superior point-level albedo and shading editing. | [
"['Alireza Moazeni' 'Shichong Peng' 'Ke Li']"
] |
null | null | 2407.00501 | null | null | http://arxiv.org/pdf/2407.00501v1 | 2024-06-29T17:56:58Z | 2024-06-29T17:56:58Z | Aeroengine performance prediction using a physical-embedded data-driven
method | Accurate and efficient prediction of aeroengine performance is of paramount importance for engine design, maintenance, and optimization endeavours. However, existing methodologies often struggle to strike an optimal balance among predictive accuracy, computational efficiency, modelling complexity, and data dependency. To address these challenges, we propose a strategy that synergistically combines domain knowledge from both the aeroengine and neural network realms to enable real-time prediction of engine performance parameters. Leveraging aeroengine domain knowledge, we judiciously design the network structure and regulate the internal information flow. Concurrently, drawing upon neural network domain expertise, we devise four distinct feature fusion methods and introduce an innovative loss function formulation. To rigorously evaluate the effectiveness and robustness of our proposed strategy, we conduct comprehensive validation across two distinct datasets. The empirical results demonstrate :(1) the evident advantages of our tailored loss function; (2) our model's ability to maintain equal or superior performance with a reduced parameter count; (3) our model's reduced data dependency compared to generalized neural network architectures; (4)Our model is more interpretable than traditional black box machine learning methods. | [
"['Tong Mo' 'Shiran Dai' 'An Fu' 'Xiaomeng Zhu' 'Shuxiao Li']"
] |
null | null | 2407.00502 | null | null | http://arxiv.org/pdf/2407.00502v1 | 2024-06-29T17:56:59Z | 2024-06-29T17:56:59Z | Deep Frequency Derivative Learning for Non-stationary Time Series
Forecasting | While most time series are non-stationary, it is inevitable for models to face the distribution shift issue in time series forecasting. Existing solutions manipulate statistical measures (usually mean and std.) to adjust time series distribution. However, these operations can be theoretically seen as the transformation towards zero frequency component of the spectrum which cannot reveal full distribution information and would further lead to information utilization bottleneck in normalization, thus hindering forecasting performance. To address this problem, we propose to utilize the whole frequency spectrum to transform time series to make full use of data distribution from the frequency perspective. We present a deep frequency derivative learning framework, DERITS, for non-stationary time series forecasting. Specifically, DERITS is built upon a novel reversible transformation, namely Frequency Derivative Transformation (FDT) that makes signals derived in the frequency domain to acquire more stationary frequency representations. Then, we propose the Order-adaptive Fourier Convolution Network to conduct adaptive frequency filtering and learning. Furthermore, we organize DERITS as a parallel-stacked architecture for the multi-order derivation and fusion for forecasting. Finally, we conduct extensive experiments on several datasets which show the consistent superiority in both time series forecasting and shift alleviation. | [
"['Wei Fan' 'Kun Yi' 'Hangting Ye' 'Zhiyuan Ning' 'Qi Zhang' 'Ning An']"
] |
null | null | 2407.00506 | null | null | http://arxiv.org/pdf/2407.00506v1 | 2024-06-29T18:19:55Z | 2024-06-29T18:19:55Z | ShapG: new feature importance method based on the Shapley value | With wide application of Artificial Intelligence (AI), it has become particularly important to make decisions of AI systems explainable and transparent. In this paper, we proposed a new Explainable Artificial Intelligence (XAI) method called ShapG (Explanations based on Shapley value for Graphs) for measuring feature importance. ShapG is a model-agnostic global explanation method. At the first stage, it defines an undirected graph based on the dataset, where nodes represent features and edges are added based on calculation of correlation coefficients between features. At the second stage, it calculates an approximated Shapley value by sampling the data taking into account this graph structure. The sampling approach of ShapG allows to calculate the importance of features efficiently, i.e. to reduce computational complexity. Comparison of ShapG with other existing XAI methods shows that it provides more accurate explanations for two examined datasets. We also compared other XAI methods developed based on cooperative game theory with ShapG in running time, and the results show that ShapG exhibits obvious advantages in its running time, which further proves efficiency of ShapG. In addition, extensive experiments demonstrate a wide range of applicability of the ShapG method for explaining complex models. We find ShapG an important tool in improving explainability and transparency of AI systems and believe it can be widely used in various fields. | [
"['Chi Zhao' 'Jing Liu' 'Elena Parilina']"
] |
null | null | 2407.00510 | null | null | http://arxiv.org/pdf/2407.00510v1 | 2024-06-29T18:44:49Z | 2024-06-29T18:44:49Z | Stochastic stem bucking using mixture density neural networks | Poor bucking decisions made by forest harvesters can have a negative effect on the products that are generated from the logs. Making the right bucking decisions is not an easy task because harvesters must rely on predictions of the stem profile for the part of the stems that is not yet measured. The goal of this project is to improve the bucking decisions made by forest harvesters with a stochastic bucking method. We developed a Long Short-Term Memory (LSTM) neural network that predicted the parameters of a Gaussian distribution conditioned on the known part of the stem, enabling the creation of multiple samples of stem profile predictions for the unknown part of the stem. The bucking decisions could then be optimized using a novel stochastic bucking algorithm which used all the stem profiles generated to choose the logs to generate from the stem. The stochastic bucking algorithm was compared to two benchmark models: A polynomial model that could not condition its predictions on more than one diameter measurement, and a deterministic LSTM neural network. All models were evaluated on stem profiles of four coniferous species prevalent in eastern Canada. In general, the best bucking decisions were taken by the stochastic LSTM models, demonstrating the usefulness of the method. The second-best results were mostly obtained by the deterministic LSTM model and the worst results by the polynomial model, corroborating the usefulness of conditioning the stem curve predictions on multiple measurements. | [
"['Simon Schmiedel']"
] |
null | null | 2407.00521 | null | null | http://arxiv.org/pdf/2407.00521v1 | 2024-06-29T19:50:06Z | 2024-06-29T19:50:06Z | A Medical Low-Back Pain Physical Rehabilitation Dataset for Human Body
Movement Analysis | While automatic monitoring and coaching of exercises are showing encouraging results in non-medical applications, they still have limitations such as errors and limited use contexts. To allow the development and assessment of physical rehabilitation by an intelligent tutoring system, we identify in this article four challenges to address and propose a medical dataset of clinical patients carrying out low back-pain rehabilitation exercises. The dataset includes 3D Kinect skeleton positions and orientations, RGB videos, 2D skeleton data, and medical annotations to assess the correctness, and error classification and localisation of body part and timespan. Along this dataset, we perform a complete research path, from data collection to processing, and finally a small benchmark. We evaluated on the dataset two baseline movement recognition algorithms, pertaining to two different approaches: the probabilistic approach with a Gaussian Mixture Model (GMM), and the deep learning approach with a Long-Short Term Memory (LSTM). This dataset is valuable because it includes rehabilitation relevant motions in a clinical setting with patients in their rehabilitation program, using a cost-effective, portable, and convenient sensor, and because it shows the potential for improvement on these challenges. | [
"['Sao Mai Nguyen' 'Maxime Devanne' 'Olivier Remy-Neris'\n 'Mathieu Lempereur' 'André Thepaut']"
] |
null | null | 2407.00524 | null | null | http://arxiv.org/pdf/2407.00524v1 | 2024-06-29T20:03:50Z | 2024-06-29T20:03:50Z | Real-Time Energy Measurement for Non-Intrusive Well-Being Monitoring of
Elderly People -- a Case Study | This article presents a case study demonstrating a non-intrusive method for the well-being monitoring of elderly people. It is based on our real-time energy measurement system, which uses tiny beacons attached to electricity meters. Four participants aged 67-82 years took part in our study. We observed their electric power consumption for approx. a month, and then we analyzed them, taking into account the participants' notes on their activities. We created typical daily usage profiles for each participant and used anomaly detection to find unusual energy consumption. We found out that real-time energy measurement can give significant insight into someone's daily activities and, consequently, bring invaluable information to caregivers about the well-being of an elderly person, while being discreet and entirely non-intrusive. | [
"['Mateusz Brzozowski' 'Artur Janicki']"
] |
null | null | 2407.00529 | null | null | http://arxiv.org/pdf/2407.00529v1 | 2024-06-29T20:56:34Z | 2024-06-29T20:56:34Z | Detecting and Identifying Selection Structure in Sequential Data | We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. Since this selection process often distorts statistical analysis, previous work primarily views it as a bias to be corrected and proposes various methods to mitigate its effect. However, while controlling this bias is crucial, selection also offers an opportunity to provide a deeper insight into the hidden generation process, as it is a fundamental mechanism underlying what we observe. In particular, overlooking selection in sequential data can lead to an incomplete or overcomplicated inductive bias in modeling, such as assuming a universal autoregressive structure for all dependencies. Therefore, rather than merely viewing it as a bias, we explore the causal structure of selection in sequential data to delve deeper into the complete causal process. Specifically, we show that selection structure is identifiable without any parametric assumptions or interventional experiments. Moreover, even in cases where selection variables coexist with latent confounders, we still establish the nonparametric identifiability under appropriate structural conditions. Meanwhile, we also propose a provably correct algorithm to detect and identify selection structures as well as other types of dependencies. The framework has been validated empirically on both synthetic data and real-world music. | [
"['Yujia Zheng' 'Zeyu Tang' 'Yiwen Qiu' 'Bernhard Schölkopf' 'Kun Zhang']"
] |
null | null | 2407.00537 | null | null | http://arxiv.org/pdf/2407.00537v1 | 2024-06-29T22:13:54Z | 2024-06-29T22:13:54Z | Accelerating Longitudinal MRI using Prior Informed Latent Diffusion | MRI is a widely used ionization-free soft-tissue imaging modality, often employed repeatedly over a patient's lifetime. However, prolonged scanning durations, among other issues, can limit availability and accessibility. In this work, we aim to substantially reduce scan times by leveraging prior scans of the same patient. These prior scans typically contain considerable shared information with the current scan, thereby enabling higher acceleration rates when appropriately utilized. We propose a prior informed reconstruction method with a trained diffusion model in conjunction with data-consistency steps. Our method can be trained with unlabeled image data, eliminating the need for a dataset of either k-space measurements or paired longitudinal scans as is required of other learning-based methods. We demonstrate superiority of our method over previously suggested approaches in effectively utilizing prior information without over-biasing prior consistency, which we validate on both an open-source dataset of healthy patients as well as several longitudinal cases of clinical interest. | [
"['Yonatan Urman' 'Zachary Shah' 'Ashwin Kumar' 'Bruno P. Soares'\n 'Kawin Setsompop']"
] |
null | null | 2407.00553 | null | null | http://arxiv.org/pdf/2407.00553v1 | 2024-06-30T01:10:13Z | 2024-06-30T01:10:13Z | Cooperative Advisory Residual Policies for Congestion Mitigation | Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these approaches are limited in practice as they assume precise control over autonomous vehicle fleets, incur extensive installation costs for a centralized sensor ecosystem, and also fail to account for uncertainty in driver behavior. To this end, we develop a class of learned residual policies that can be used in cooperative advisory systems and only require the use of a single vehicle with a human driver. Our policies advise drivers to behave in ways that mitigate traffic congestion while accounting for diverse driver behaviors, particularly drivers' reactions to instructions, to provide an improved user experience. To realize such policies, we introduce an improved reward function that explicitly addresses congestion mitigation and driver attitudes to advice. We show that our residual policies can be personalized by conditioning them on an inferred driver trait that is learned in an unsupervised manner with a variational autoencoder. Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers. Our results show that our approaches successfully mitigate congestion while adapting to different driver behaviors, with up to 20% and 40% improvement as measured by a combination metric of speed and deviations in speed across time over baselines in our simulation tests and user study, respectively. Our user study further shows that our policies are human-compatible and personalize to drivers. | [
"['Aamir Hasan' 'Neeloy Chakraborty' 'Haonan Chen' 'Jung-Hoon Cho'\n 'Cathy Wu' 'Katherine Driggs-Campbell']"
] |
null | null | 2407.00567 | null | null | http://arxiv.org/pdf/2407.00567v1 | 2024-06-30T02:43:15Z | 2024-06-30T02:43:15Z | A Contextual Combinatorial Bandit Approach to Negotiation | Learning effective negotiation strategies poses two key challenges: the exploration-exploitation dilemma and dealing with large action spaces. However, there is an absence of learning-based approaches that effectively address these challenges in negotiation. This paper introduces a comprehensive formulation to tackle various negotiation problems. Our approach leverages contextual combinatorial multi-armed bandits, with the bandits resolving the exploration-exploitation dilemma, and the combinatorial nature handles large action spaces. Building upon this formulation, we introduce NegUCB, a novel method that also handles common issues such as partial observations and complex reward functions in negotiation. NegUCB is contextual and tailored for full-bandit feedback without constraints on the reward functions. Under mild assumptions, it ensures a sub-linear regret upper bound. Experiments conducted on three negotiation tasks demonstrate the superiority of our approach. | [
"['Yexin Li' 'Zhancun Mu' 'Siyuan Qi']"
] |
null | null | 2407.00568 | null | null | http://arxiv.org/pdf/2407.00568v2 | 2024-07-02T02:54:49Z | 2024-06-30T02:50:28Z | Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep
Penalty Neural Ordinary Differential Equations | Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as the geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerating the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE(MP-NODE), is applied to chaotic systems such as the Kuramoto-Sivashinsky equation and the two-dimensional Kolmogorov flow. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics. | [
"['Dibyajyoti Chakraborty' 'Seung Whan Chung' 'Romit Maulik']"
] |
null | null | 2407.00571 | null | null | http://arxiv.org/pdf/2407.00571v1 | 2024-06-30T03:14:17Z | 2024-06-30T03:14:17Z | Adversarial Online Learning with Temporal Feedback Graphs | We study a variant of prediction with expert advice where the learner's action at round $t$ is only allowed to depend on losses on a specific subset of the rounds (where the structure of which rounds' losses are visible at time $t$ is provided by a directed "feedback graph" known to the learner). We present a novel learning algorithm for this setting based on a strategy of partitioning the losses across sub-cliques of this graph. We complement this with a lower bound that is tight in many practical settings, and which we conjecture to be within a constant factor of optimal. For the important class of transitive feedback graphs, we prove that this algorithm is efficiently implementable and obtains the optimal regret bound (up to a universal constant). | [
"['Khashayar Gatmiry' 'Jon Schneider']"
] |
null | null | 2407.00575 | null | null | http://arxiv.org/pdf/2407.00575v1 | 2024-06-30T03:33:42Z | 2024-06-30T03:33:42Z | Learning to Control Unknown Strongly Monotone Games | Consider $N$ players each with a $d$-dimensional action set. Each of the players' utility functions includes their reward function and a linear term for each dimension, with coefficients that are controlled by the manager. We assume that the game is strongly monotone, so if each player runs gradient descent, the dynamics converge to a unique Nash equilibrium (NE). The NE is typically inefficient in terms of global performance. The resulting global performance of the system can be improved by imposing $K$-dimensional linear constraints on the NE. We therefore want the manager to pick the controlled coefficients that impose the desired constraint on the NE. However, this requires knowing the players' reward functions and their action sets. Obtaining this game structure information is infeasible in a large-scale network and violates the users' privacy. To overcome this, we propose a simple algorithm that learns to shift the NE of the game to meet the linear constraints by adjusting the controlled coefficients online. Our algorithm only requires the linear constraints violation as feedback and does not need to know the reward functions or the action sets. We prove that our algorithm, which is based on two time-scale stochastic approximation, guarantees convergence with probability 1 to the set of NE that meet target linear constraints. We then provide a mean square convergence rate of $O(t^{-1/4})$ for our algorithm. This is the first such bound for two time-scale stochastic approximation where the slower time-scale is a fixed point iteration with a non-expansive mapping. We demonstrate how our scheme can be applied to optimizing a global quadratic cost at NE and load balancing in resource allocation games. We provide simulations of our algorithm for these scenarios. | [
"['Siddharth Chandak' 'Ilai Bistritz' 'Nicholas Bambos']"
] |
null | null | 2407.00584 | null | null | http://arxiv.org/pdf/2407.00584v1 | 2024-06-30T04:15:03Z | 2024-06-30T04:15:03Z | Hyperparameter Optimization for Randomized Algorithms: A Case Study for
Random Features | Randomized algorithms exploit stochasticity to reduce computational complexity. One important example is random feature regression (RFR) that accelerates Gaussian process regression (GPR). RFR approximates an unknown function with a random neural network whose hidden weights and biases are sampled from a probability distribution. Only the final output layer is fit to data. In randomized algorithms like RFR, the hyperparameters that characterize the sampling distribution greatly impact performance, yet are not directly accessible from samples. This makes optimization of hyperparameters via standard (gradient-based) optimization tools inapplicable. Inspired by Bayesian ideas from GPR, this paper introduces a random objective function that is tailored for hyperparameter tuning of vector-valued random features. The objective is minimized with ensemble Kalman inversion (EKI). EKI is a gradient-free particle-based optimizer that is scalable to high-dimensions and robust to randomness in objective functions. A numerical study showcases the new black-box methodology to learn hyperparameter distributions in several problems that are sensitive to the hyperparameter selection: two global sensitivity analyses, integrating a chaotic dynamical system, and solving a Bayesian inverse problem from atmospheric dynamics. The success of the proposed EKI-based algorithm for RFR suggests its potential for automated optimization of hyperparameters arising in other randomized algorithms. | [
"['Oliver R. A. Dunbar' 'Nicholas H. Nelsen' 'Maya Mutic']"
] |
null | null | 2407.00599 | null | null | http://arxiv.org/pdf/2407.00599v2 | 2024-07-03T01:51:11Z | 2024-06-30T05:55:11Z | Parm: Efficient Training of Large Sparsely-Activated Models with
Dedicated Schedules | Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in computation demands. Despite the wide adoption of hybrid parallel paradigms like model parallelism, expert parallelism, and expert-sharding parallelism (i.e., MP+EP+ESP) to support MoE model training on GPU clusters, the training efficiency is hindered by communication costs introduced by these parallel paradigms. To address this limitation, we propose Parm, a system that accelerates MP+EP+ESP training by designing two dedicated schedules for placing communication tasks. The proposed schedules eliminate redundant computations and communications and enable overlaps between intra-node and inter-node communications, ultimately reducing the overall training time. As the two schedules are not mutually exclusive, we provide comprehensive theoretical analyses and derive an automatic and accurate solution to determine which schedule should be applied in different scenarios. Experimental results on an 8-GPU server and a 32-GPU cluster demonstrate that Parm outperforms the state-of-the-art MoE training system, DeepSpeed-MoE, achieving 1.13$times$ to 5.77$times$ speedup on 1296 manually configured MoE layers and approximately 3$times$ improvement on two real-world MoE models based on BERT and GPT-2. | [
"['Xinglin Pan' 'Wenxiang Lin' 'Shaohuai Shi' 'Xiaowen Chu' 'Weinong Sun'\n 'Bo Li']"
] |
null | null | 2407.00609 | null | null | http://arxiv.org/pdf/2407.00609v1 | 2024-06-30T06:58:04Z | 2024-06-30T06:58:04Z | ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene
Understanding | Scene graphs have been proven to be useful for various scene understanding tasks due to their compact and explicit nature. However, existing approaches often neglect the importance of maintaining the symmetry-preserving property when generating scene graphs from 3D point clouds. This oversight can diminish the accuracy and robustness of the resulting scene graphs, especially when handling noisy, multi-view 3D data. This work, to the best of our knowledge, is the first to implement an Equivariant Graph Neural Network in semantic scene graph generation from 3D point clouds for scene understanding. Our proposed method, ESGNN, outperforms existing state-of-the-art approaches, demonstrating a significant improvement in scene estimation with faster convergence. ESGNN demands low computational resources and is easy to implement from available frameworks, paving the way for real-time applications such as robotics and computer vision. | [
"['Quang P. M. Pham' 'Khoi T. N. Nguyen' 'Lan C. Ngo' 'Truong Do'\n 'Truong Son Hy']"
] |
null | null | 2407.00610 | null | null | http://arxiv.org/pdf/2407.00610v1 | 2024-06-30T06:58:31Z | 2024-06-30T06:58:31Z | Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization | Black-box optimization (BBO) aims to optimize an objective function by iteratively querying a black-box oracle. This process demands sample-efficient optimization due to the high computational cost of function evaluations. While prior studies focus on forward approaches to learn surrogates for the unknown objective function, they struggle with high-dimensional inputs where valid inputs form a small subspace (e.g., valid protein sequences), which is common in real-world tasks. Recently, diffusion models have demonstrated impressive capability in learning the high-dimensional data manifold. They have shown promising performance in black-box optimization tasks but only in offline settings. In this work, we propose diffusion-based inverse modeling for black-box optimization (Diff-BBO), the first inverse approach leveraging diffusion models for online BBO problem. Diff-BBO distinguishes itself from forward approaches through the design of acquisition function. Instead of proposing candidates in the design space, Diff-BBO employs a novel acquisition function Uncertainty-aware Exploration (UaE) to propose objective function values, which leverages the uncertainty of a conditional diffusion model to generate samples in the design space. Theoretically, we prove that using UaE leads to optimal optimization outcomes. Empirically, we redesign experiments on the Design-Bench benchmark for online settings and show that Diff-BBO achieves state-of-the-art performance. | [
"['Dongxia Wu' 'Nikki Lijing Kuang' 'Ruijia Niu' 'Yi-An Ma' 'Rose Yu']"
] |
null | null | 2407.00613 | null | null | http://arxiv.org/abs/2407.00613v1 | 2024-06-30T07:11:00Z | 2024-06-30T07:11:00Z | A Linear Programming Enhanced Genetic Algorithm for Hyperparameter
Tuning in Machine Learning | In this paper, we formulate the hyperparameter tuning problem in machine learning as a bilevel program. The bilevel program is solved using a micro genetic algorithm that is enhanced with a linear program. While the genetic algorithm searches over discrete hyperparameters, the linear program enhancement allows hyper local search over continuous hyperparameters. The major contribution in this paper is the formulation of a linear program that supports fast search over continuous hyperparameters, and can be integrated with any hyperparameter search technique. It can also be applied directly on any trained machine learning or deep learning model for the purpose of fine-tuning. We test the performance of the proposed approach on two datasets, MNIST and CIFAR-10. Our results clearly demonstrate that using the linear program enhancement offers significant promise when incorporated with any population-based approach for hyperparameter tuning. | [
"['Ankur Sinha' 'Paritosh Pankaj']"
] |
null | null | 2407.00615 | null | null | http://arxiv.org/pdf/2407.00615v1 | 2024-06-30T07:47:34Z | 2024-06-30T07:47:34Z | GC-Bench: An Open and Unified Benchmark for Graph Condensation | Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core concept of GC is to create a smaller, more manageable graph that retains the characteristics of the original graph. Despite the proliferation of graph condensation methods developed in recent years, there is no comprehensive evaluation and in-depth analysis, which creates a great obstacle to understanding the progress in this field. To fill this gap, we develop a comprehensive Graph Condensation Benchmark (GC-Bench) to analyze the performance of graph condensation in different scenarios systematically. Specifically, GC-Bench systematically investigates the characteristics of graph condensation in terms of the following dimensions: effectiveness, transferability, and complexity. We comprehensively evaluate 12 state-of-the-art graph condensation algorithms in node-level and graph-level tasks and analyze their performance in 12 diverse graph datasets. Further, we have developed an easy-to-use library for training and evaluating different GC methods to facilitate reproducible research. The GC-Bench library is available at https://github.com/RingBDStack/GC-Bench. | [
"['Qingyun Sun' 'Ziying Chen' 'Beining Yang' 'Cheng Ji' 'Xingcheng Fu'\n 'Sheng Zhou' 'Hao Peng' 'Jianxin Li' 'Philip S. Yu']"
] |
null | null | 2407.00616 | null | null | http://arxiv.org/pdf/2407.00616v1 | 2024-06-30T07:55:32Z | 2024-06-30T07:55:32Z | DADEE: Well-calibrated uncertainty quantification in neural networks for
barriers-based robot safety | Uncertainty-aware controllers that guarantee safety are critical for safety critical applications. Among such controllers, Control Barrier Functions (CBFs) based approaches are popular because they are fast, yet safe. However, most such works depend on Gaussian Processes (GPs) or MC-Dropout for learning and uncertainty estimation, and both approaches come with drawbacks: GPs are non-parametric methods that are slow, while MC-Dropout does not capture aleatoric uncertainty. On the other hand, modern Bayesian learning algorithms have shown promise in uncertainty quantification. The application of modern Bayesian learning methods to CBF-based controllers has not yet been studied. We aim to fill this gap by surveying uncertainty quantification algorithms and evaluating them on CBF-based safe controllers. We find that model variance-based algorithms (for example, Deep ensembles, MC-dropout, etc.) and direct estimation-based algorithms (such as DEUP) have complementary strengths. Algorithms in the former category can only estimate uncertainty accurately out-of-domain, while those in the latter category can only do so in-domain. We combine the two approaches to obtain more accurate uncertainty estimates both in- and out-of-domain. As measured by the failure rate of a simulated robot, this results in a safer CBF-based robot controller. | [
"['Masoud Ataei' 'Vikas Dhiman']"
] |
null | null | 2407.00617 | null | null | http://arxiv.org/pdf/2407.00617v2 | 2024-07-07T09:51:26Z | 2024-06-30T08:00:34Z | Iterative Nash Policy Optimization: Aligning LLMs with General
Preferences via No-Regret Learning | Reinforcement Learning with Human Feedback (RLHF) has achieved great success in aligning large language models (LLMs) with human preferences. Prevalent RLHF approaches are reward-based, following the Bradley-Terry (BT) model assumption, which may not fully capture the complexity of human preferences. In this paper, we explore RLHF under a general preference framework and approach it from a game-theoretic perspective. Specifically, we formulate the problem as a two-player game and propose a novel algorithm, iterative Nash policy optimization (INPO). The key idea is to let the policy play against itself via no-regret learning, thereby approximating the Nash policy. Unlike previous methods, INPO bypasses the need for estimating the expected win rate for individual responses, which typically incurs high computational or annotation costs. Instead, we introduce a new loss objective that is directly minimized over a preference dataset. We provide theoretical analysis for our approach and demonstrate its effectiveness through experiments on various representative benchmarks. With an LLaMA-3-8B-based SFT model, INPO achieves a 41.5% length-controlled win rate on AlpacaEval 2.0 and a 38.3% win rate on Arena-Hard, showing substantial improvement over the state-of-the-art iterative algorithm [Dong et al., 2024] under the BT model assumption. Additionally, our ablation study highlights the benefits of incorporating KL regularization for response length control. | [
"['Yuheng Zhang' 'Dian Yu' 'Baolin Peng' 'Linfeng Song' 'Ye Tian'\n 'Mingyue Huo' 'Nan Jiang' 'Haitao Mi' 'Dong Yu']"
] |
null | null | 2407.00626 | null | null | http://arxiv.org/pdf/2407.00626v1 | 2024-06-30T08:52:17Z | 2024-06-30T08:52:17Z | Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with
Energy-Based Models | We present a maximum entropy inverse reinforcement learning (IRL) approach for improving the sample quality of diffusion generative models, especially when the number of generation time steps is small. Similar to how IRL trains a policy based on the reward function learned from expert demonstrations, we train (or fine-tune) a diffusion model using the log probability density estimated from training data. Since we employ an energy-based model (EBM) to represent the log density, our approach boils down to the joint training of a diffusion model and an EBM. Our IRL formulation, named Diffusion by Maximum Entropy IRL (DxMI), is a minimax problem that reaches equilibrium when both models converge to the data distribution. The entropy maximization plays a key role in DxMI, facilitating the exploration of the diffusion model and ensuring the convergence of the EBM. We also propose Diffusion by Dynamic Programming (DxDP), a novel reinforcement learning algorithm for diffusion models, as a subroutine in DxMI. DxDP makes the diffusion model update in DxMI efficient by transforming the original problem into an optimal control formulation where value functions replace back-propagation in time. Our empirical studies show that diffusion models fine-tuned using DxMI can generate high-quality samples in as few as 4 and 10 steps. Additionally, DxMI enables the training of an EBM without MCMC, stabilizing EBM training dynamics and enhancing anomaly detection performance. | [
"['Sangwoong Yoon' 'Himchan Hwang' 'Dohyun Kwon' 'Yung-Kyun Noh'\n 'Frank C. Park']"
] |
null | null | 2407.00631 | null | null | http://arxiv.org/pdf/2407.00631v1 | 2024-06-30T09:13:10Z | 2024-06-30T09:13:10Z | TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial
Datasets | Clinical trials are pivotal for developing new medical treatments, yet they typically pose some risks such as patient mortality, adverse events, and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to forecast or simulate key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise and a deep understanding of trial designs have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development. The curated dataset, metrics, and basic models are publicly available at https://github.com/ML2Health/ML2ClinicalTrials/tree/main/AI4Trial. | [
"['Jintai Chen' 'Yaojun Hu' 'Yue Wang' 'Yingzhou Lu' 'Xu Cao' 'Miao Lin'\n 'Hongxia Xu' 'Jian Wu' 'Cao Xiao' 'Jimeng Sun' 'Lucas Glass'\n 'Kexin Huang' 'Marinka Zitnik' 'Tianfan Fu']"
] |
null | null | 2407.00634 | null | null | http://arxiv.org/pdf/2407.00634v1 | 2024-06-30T09:21:01Z | 2024-06-30T09:21:01Z | Tarsier: Recipes for Training and Evaluating Large Video Description
Models | Generating fine-grained video descriptions is a fundamental challenge in video understanding. In this work, we introduce Tarsier, a family of large-scale video-language models designed to generate high-quality video descriptions. Tarsier employs CLIP-ViT to encode frames separately and then uses an LLM to model temporal relationships. Despite its simple architecture, we demonstrate that with a meticulously designed two-stage training procedure, the Tarsier models exhibit substantially stronger video description capabilities than any existing open-source model, showing a $+51.4%$ advantage in human side-by-side evaluation over the strongest model. Additionally, they are comparable to state-of-the-art proprietary models, with a $+12.3%$ advantage against GPT-4V and a $-6.7%$ disadvantage against Gemini 1.5 Pro. Besides video description, Tarsier proves to be a versatile generalist model, achieving new state-of-the-art results across nine public benchmarks, including multi-choice VQA, open-ended VQA, and zero-shot video captioning. Our second contribution is the introduction of a new benchmark for evaluating video description models, consisting of a new challenging dataset featuring videos from diverse sources and varying complexity, along with an automatic method specifically designed to assess the quality of fine-grained video descriptions. We make our models and evaluation benchmark publicly available at url{https://github.com/bytedance/tarsier}. | [
"['Jiawei Wang' 'Liping Yuan' 'Yuchen Zhang']"
] |
null | null | 2407.00641 | null | null | http://arxiv.org/pdf/2407.00641v1 | 2024-06-30T09:51:58Z | 2024-06-30T09:51:58Z | HASNAS: A Hardware-Aware Spiking Neural Architecture Search Framework
for Neuromorphic Compute-in-Memory Systems | Spiking Neural Networks (SNNs) have shown capabilities for solving diverse machine learning tasks with ultra-low-power/energy computation. To further improve the performance and efficiency of SNN inference, the Compute-in-Memory (CIM) paradigm with emerging device technologies such as resistive random access memory is employed. However, most of SNN architectures are developed without considering constraints from the application and the underlying CIM hardware (e.g., memory, area, latency, and energy consumption). Moreover, most of SNN designs are derived from the Artificial Neural Networks, whose network operations are different from SNNs. These limitations hinder SNNs from reaching their full potential in accuracy and efficiency. Toward this, we propose HASNAS, a novel hardware-aware spiking neural architecture search (NAS) framework for neuromorphic CIM systems that finds an SNN that offers high accuracy under the given memory, area, latency, and energy constraints. To achieve this, HASNAS employs the following key steps: (1) optimizing SNN operations to achieve high accuracy, (2) developing an SNN architecture that facilitates an effective learning process, and (3) devising a systematic hardware-aware search algorithm to meet the constraints. The experimental results show that our HASNAS quickly finds an SNN that maintains high accuracy compared to the state-of-the-art by up to 11x speed-up, and meets the given constraints: 4x10^6 parameters of memory, 100mm^2 of area, 400ms of latency, and 120uJ energy consumption for CIFAR10 and CIFAR100; while the state-of-the-art fails to meet the constraints. In this manner, our HASNAS can enable efficient design automation for providing high-performance and energy-efficient neuromorphic CIM systems for diverse applications. | [
"['Rachmad Vidya Wicaksana Putra' 'Muhammad Shafique']"
] |
null | null | 2407.00644 | null | null | http://arxiv.org/pdf/2407.00644v1 | 2024-06-30T10:11:18Z | 2024-06-30T10:11:18Z | Clusterpath Gaussian Graphical Modeling | Graphical models serve as effective tools for visualizing conditional dependencies between variables. However, as the number of variables grows, interpretation becomes increasingly difficult, and estimation uncertainty increases due to the large number of parameters relative to the number of observations. To address these challenges, we introduce the Clusterpath estimator of the Gaussian Graphical Model (CGGM) that encourages variable clustering in the graphical model in a data-driven way. Through the use of a clusterpath penalty, we group variables together, which in turn results in a block-structured precision matrix whose block structure remains preserved in the covariance matrix. We present a computationally efficient implementation of the CGGM estimator by using a cyclic block coordinate descent algorithm. In simulations, we show that CGGM not only matches, but oftentimes outperforms other state-of-the-art methods for variable clustering in graphical models. We also demonstrate CGGM's practical advantages and versatility on a diverse collection of empirical applications. | [
"['D. J. W. Touw' 'A. Alfons' 'P. J. F. Groenen' 'I. Wilms']"
] |
null | null | 2407.00649 | null | null | http://arxiv.org/pdf/2407.00649v1 | 2024-06-30T10:21:41Z | 2024-06-30T10:21:41Z | Particle Semi-Implicit Variational Inference | Semi-implicit variational inference (SIVI) enriches the expressiveness of variational families by utilizing a kernel and a mixing distribution to hierarchically define the variational distribution. Existing SIVI methods parameterize the mixing distribution using implicit distributions, leading to intractable variational densities. As a result, directly maximizing the evidence lower bound (ELBO) is not possible and so, they resort to either: optimizing bounds on the ELBO, employing costly inner-loop Markov chain Monte Carlo runs, or solving minimax objectives. In this paper, we propose a novel method for SIVI called Particle Variational Inference (PVI) which employs empirical measures to approximate the optimal mixing distributions characterized as the minimizer of a natural free energy functional via a particle approximation of an Euclidean--Wasserstein gradient flow. This approach means that, unlike prior works, PVI can directly optimize the ELBO; furthermore, it makes no parametric assumption about the mixing distribution. Our empirical results demonstrate that PVI performs favourably against other SIVI methods across various tasks. Moreover, we provide a theoretical analysis of the behaviour of the gradient flow of a related free energy functional: establishing the existence and uniqueness of solutions as well as propagation of chaos results. | [
"['Jen Ning Lim' 'Adam M. Johansen']"
] |
null | null | 2407.00657 | null | null | http://arxiv.org/pdf/2407.00657v1 | 2024-06-30T11:00:09Z | 2024-06-30T11:00:09Z | Improving Real-Time Music Accompaniment Separation with MMDenseNet | Music source separation aims to separate polyphonic music into different types of sources. Most existing methods focus on enhancing the quality of separated results by using a larger model structure, rendering them unsuitable for deployment on edge devices. Moreover, these methods may produce low-quality output when the input duration is short, making them impractical for real-time applications. Therefore, the goal of this paper is to enhance a lightweight model, MMDenstNet, to strike a balance between separation quality and latency for real-time applications. Different directions of improvement are explored or proposed in this paper, including complex ideal ratio mask, self-attention, band-merge-split method, and feature look back. Source-to-distortion ratio, real-time factor, and optimal latency are employed to evaluate the performance. To align with our application requirements, the evaluation process in this paper focuses on the separation performance of the accompaniment part. Experimental results demonstrate that our improvement achieves low real-time factor and optimal latency while maintaining acceptable separation quality. | [
"['Chun-Hsiang Wang' 'Chung-Che Wang' 'Jun-You Wang' 'Jyh-Shing Roger Jang'\n 'Yen-Hsun Chu']"
] |
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