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Found 145 papers, 73 papers with code
A new perspective on brain stimulation interventions: Optimal stochastic tracking control of brain network dynamics
Results show that the energy associated with optimal stochastic tracking control is negatively correlated with the intrinsic average controllability of the brain network system, while the energy of the optimal state approaching control is significantly related to the target state value.
Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval
To address such problems, an instruction-tuned Large language model (LLM) with a cross-encoder architecture could be a reasonable choice.
A Large-scale Empirical Study on Large Language Models for Election Prediction
In the first part of this paper, we explore and introduce a multi-step reasoning framework for election prediction, which systematically integrates demographic, ideological, and time-sensitive factors.
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring.
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection
Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream analysis, recommendation systems, and social network moderation.
Political-LLM: Large Language Models in Political Science
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection.
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications
While Hierarchical Federated Learning (HFL) improves practicality in multi-tier IoT environments by multi-layer aggregation, it still faces challenges in communication efficiency and accuracy due to high data transfer volumes, data heterogeneity, and imbalanced device distribution, struggling to meet the low-latency and high-accuracy model training requirements of practical IoT scenarios.
Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation
Large Language Models (LLM) are usually fine-tuned to participate in dyadic or two-party dialogues, which can not adapt well to multi-party dialogues (MPD), which hinders their applications in such scenarios including multi-personal meetings, discussions and daily communication.
NLP-ADBench: NLP Anomaly Detection Benchmark
This work fills a crucial gap in the field and establishes a foundation for advancing NLP anomaly detection, particularly in the context of improving the safety and reliability of web-based systems.
Personalized Multimodal Large Language Models: A Survey
We propose an intuitive taxonomy for categorizing the techniques used to personalize MLLMs to individual users, and discuss the techniques accordingly.
Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing
Recent advancements in diffusion models have made generative image editing more accessible, enabling creative edits but raising ethical concerns, particularly regarding malicious edits to human portraits that threaten privacy and identity security.
DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration
Recent advancements in Large Language Models (LLMs) have opened new avenues for accelerating drug discovery processes.
COOD: Concept-based Zero-shot OOD Detection
Existing OOD detection methods struggle to capture the intricate semantic relationships and label co-occurrences inherent in multi-label settings, often requiring large amounts of training data and failing to generalize to unseen label combinations.
DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection
Extensive experiments on two tasks, five datasets, and nine base OOD algorithms demonstrate that DPU significantly improves OOD detection performance, setting a new state-of-the-art in multimodal OOD detection, with improvements of up to 80 percent in Far-OOD detection.
Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments
In this work, we address the challenge of selecting optimal acceleration methods in decentralized systems by introducing a meta-learning-based framework.
Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models
Can Large Language Models (LLMs) accurately predict election outcomes?
LEGO-Learn: Label-Efficient Graph Open-Set Learning
Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to address this challenge by training models that can accurately classify known, in-distribution (ID) classes while identifying and handling previously unseen classes during inference.
Movie Gen: A Cast of Media Foundation Models
Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation.
Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation
Despite the advancements of large language models (LLMs) in general generation tasks, their potential in facilitating the generation of synthetic clinical trials is under-explored.
BA-Net: Bridge Attention in Deep Neural Networks
Attention mechanisms, particularly channel attention, have become highly influential in numerous computer vision tasks.
MetaOOD: Automatic Selection of OOD Detection Models
How can we automatically select an out-of-distribution (OOD) detection model for various underlying tasks?
DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning
In recent years, deep learning has revolutionized the field of protein science, enabling advancements in predicting protein properties, structural folding and interactions.
MeTHanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning
Large Language Model can reasonably understand and generate human expressions but may lack of thorough thinking and reasoning mechanisms.
Conditional nonparametric variable screening by neural factor regression
Our test statistics are based on the estimated partial derivative of the regression function of the candidate variable for screening and a observable proxy for the latent factors.
Model Agnostic Hybrid Sharding For Heterogeneous Distributed Inference
Nesa introduces a model-agnostic sharding framework designed for decentralized AI inference.
Towards Secure and Private AI: A Framework for Decentralized Inference
This framework aims to enhance security and privacy and improve the reliability and fairness of multimodal AI systems.
Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction
This article presents Appformer, a novel mobile application prediction framework inspired by the efficiency of Transformer-like architectures in processing sequential data through self-attention mechanisms.
ViTime: A Visual Intelligence-Based Foundation Model for Time Series Forecasting
The success of large pretrained models in natural language processing (NLP) and computer vision (CV) has opened new avenues for constructing foundation models for time series forecasting (TSF).
TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era
We have made use of the REDVID simulation framework, as well as reductions applied to the TrackML data set, to compose five data sets from simple to complex, for our experiments.
GUICourse: From General Vision Language Models to Versatile GUI Agents
Utilizing Graphic User Interface (GUI) for human-computer interaction is essential for accessing a wide range of digital tools.
Ranked #15 on
Natural Language Visual Grounding
on ScreenSpot
Natural Language Visual Grounding
Optical Character Recognition (OCR)
I Don't Know You, But I Can Catch You: Real-Time Defense against Diverse Adversarial Patches for Object Detectors
Most defenses are only effective against the HA, leaving the detector vulnerable to the AA.
Image and Video Tokenization with Binary Spherical Quantization
The resulting BSQ-ViT achieves state-of-the-art visual reconstruction quality on image and video reconstruction benchmarks with 2. 4$\times$ throughput compared to the best prior methods.
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
Molecule synthesis through machine learning is one of the fundamental problems in drug discovery.
Hidden in Plain Sight: Exploring Chat History Tampering in Interactive Language Models
Large Language Models (LLMs) such as ChatGPT and Llama have become prevalent in real-world applications, exhibiting impressive text generation performance.
Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering
This research sheds light on previously unexplored methods and provides valuable insights for the field of particle track reconstruction and hit clustering in HEP.
MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities
Extensive experiments on MultiOOD demonstrate that training with A2D and NP-Mix improves existing OOD detection algorithms by a large margin.
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems
Machine learning (ML) methods are widely used in industrial applications, which usually require a large amount of training data.
Bayesian Diffusion Models for 3D Shape Reconstruction
We present Bayesian Diffusion Models (BDM), a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes.
VideoPrism: A Foundational Visual Encoder for Video Understanding
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model.
Sparse Anatomical Prompt Semi-Supervised Learning with Masked Image Modeling for CBCT Tooth Segmentation
To address these challenges, this study aims to propose a tasked-oriented Masked Auto-Encoder paradigm to effectively utilize large amounts of unlabeled data to achieve accurate tooth segmentation with limited labeled data.
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
Distilling Vision-Language Models on Millions of Videos
Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
LLM Factoscope: Uncovering LLMs' Factual Discernment through Inner States Analysis
In this paper, we introduce the LLM factoscope, a novel Siamese network-based model that leverages the inner states of LLMs for factual detection.
GenoCraft: A Comprehensive, User-Friendly Web-Based Platform for High-Throughput Omics Data Analysis and Visualization
With GenoCraft, researchers and data scientists have access to an array of cutting-edge bioinformatics tools under a user-friendly interface, making it a valuable resource for managing and analyzing large-scale omics data.
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the case for semi-supervised anomaly detection.
Benchmarking Machine Learning Models for Quantum Error Correction
Quantum Error Correction (QEC) is one of the fundamental problems in quantum computer systems, which aims to detect and correct errors in the data qubits within quantum computers.
What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?
With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention.
Improving Discriminative Multi-Modal Learning with Large-Scale Pre-Trained Models
To this end, we introduce Multi-Modal Low-Rank Adaptation learning (MMLoRA).
LEAP: Liberate Sparse-view 3D Modeling from Camera Poses
Are camera poses necessary for multi-view 3D modeling?
Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants
The capabilities of MLLMs depend on two crucial factors: the model architecture to facilitate the feature alignment of visual modules and large language models; the multimodal instruction tuning datasets for human instruction following.
Training a Large Video Model on a Single Machine in a Day
Videos are big, complex to pre-process, and slow to train on.
Ranked #1 on
Action Recognition
on EPIC-KITCHENS-100
(using extra training data)
ADGym: Design Choices for Deep Anomaly Detection
Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing.
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text.
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
Building a competitive counterpart in other languages is highly challenging due to the low-resource nature of non-English multimodal data (i. e., lack of large-scale, high-quality image-text data).
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks
Outlier detection (OD) finds many applications with a rich literature of numerous techniques.
Accurate 3D Prediction of Missing Teeth in Diverse Patterns for Precise Dental Implant Planning
This study presents a novel framework for accurate prediction of missing teeth in different patterns, facilitating digital implant planning.
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection
DSV captures the alignment between an augmentation function and the anomaly-generating mechanism with surrogate losses, which approximate the discordance and separability of test data, respectively.
Regularized Multivariate Functional Principal Component Analysis
Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data.
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures.
Construction of unbiased dental template and parametric dental model for precision digital dentistry
In this study, we develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation.
Stabilization with Prescribed Instant via Lyapunov Method
In anothor word, the settling time under the presented controller is independent of the initial conditions and equals the prescribed time instant.
Weakly Supervised Anomaly Detection: A Survey
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression
Federated learning has become a popular tool in the big data era nowadays.
Online Kernel Sliced Inverse Regression
Online dimension reduction is a common method for high-dimensional streaming data processing.
EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation
We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state (NESS) systems.
Learning Video Representations from Large Language Models
We introduce LaViLa, a new approach to learning video-language representations by leveraging Large Language Models (LLMs).
Ranked #1 on
Action Recognition
on Charades-Ego
Offline Supervised Learning V.S. Online Direct Policy Optimization: A Comparative Study and A Unified Training Paradigm for Neural Network-Based Optimal Feedback Control
We first conduct a comparative study of two prevalent approaches: offline supervised learning and online direct policy optimization.
Toward Unsupervised Outlier Model Selection
At its core, ELECT is based on meta-learning; transferring prior knowledge (e. g. model performance) on historical datasets that are similar to the new one to facilitate UOMS.
Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks
Deep learning models have achieved excellent recognition results on large-scale video benchmarks.
Real-time Online Video Detection with Temporal Smoothing Transformers
Streaming video recognition reasons about objects and their actions in every frame of a video.
Diffusion Models: A Comprehensive Survey of Methods and Applications
This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration.
Hyperparameter Optimization for Unsupervised Outlier Detection
Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without any labels?
ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels
In this work, we propose a method to leverage weak/noisy labels (e. g., risk scores generated by machine rules for detecting malware) that are cheaper to obtain for anomaly detection.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
ADBench: Anomaly Detection Benchmark
Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data?
ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training
Based on the observation, we present a method, called Ensemble Logit Difference Inhibition (ELODI), to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model.
Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis
Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i. e., hashing-based cross-modal medical data retrieval), provides a new view to promot computeraided diagnosis.
A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images
Inspired by the pathologist's clinical diagnosis process, we propose a weakly supervised deep reinforcement learning framework, which can greatly reduce the time required for network inference.
Clues Before Answers: Generation-Enhanced Multiple-Choice QA
A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework.
Ranked #10 on
Question Answering
on OpenBookQA
Gaussian Kernel Variance For an Adaptive Learning Method on Signals Over Graphs
To be more specific, we focus on SKG with a Gaussian kernel and specify how to find a suitable variance for the kernel.
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation
The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i. e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation.
Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics
In this work, we design a novel, scalable approach, where a general demand forecasting model is built using the combined data of all the companies with a normalization factor.
Learning Robust Representation through Graph Adversarial Contrastive Learning
Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matrix and node features.
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
To address these issues, we present a simple yet effective algorithm called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the "rare events" that appear in the tails of a distribution.
AI-Lancet: Locating Error-inducing Neurons to Optimize Neural Networks
With the knowledge of error-inducing neurons, we propose two methods to fix the errors: the neuron-flip and the neuron-fine-tuning.
BA-Net: Bridge Attention for Deep Convolutional Neural Networks
The core idea of this design is to bridge the outputs of the previous convolution layers through skip connections for channel weights generation.
Automatic Unsupervised Outlier Model Selection
Given an unsupervised outlier detection task on a new dataset, how can we automatically select a good outlier detection algorithm and its hyperparameter(s) (collectively called a model)?
Federated Learning Based on Dynamic Regularization
We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round.
TOD: GPU-accelerated Outlier Detection via Tensor Operations
Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection.
3D Dental model segmentation with graph attentional convolution network
We have evaluated our network on a real-patient dataset of dental models acquired through 3D intraoral scanners, and experimental results show that our method outperforms state-of-the-art deep learning methods for 3D shape segmentation.
Local Patch Network with Global Attention for Infrared Small Target Detection
A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images.
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning
In vision-based reinforcement learning (RL) tasks, it is prevalent to assign auxiliary tasks with a surrogate self-supervised loss so as to obtain more semantic representations and improve sample efficiency.
TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation
State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.
GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising
To address this issue, we propose a novel graph convolutional network-based LDCT denoising model, namely GCN-MIF, to explicitly perform multi-information fusion for denoising purpose.
Revisiting Skeleton-based Action Recognition
In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.
Ranked #1 on
Action Recognition
on Volleyball
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?
These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models.
PointBA: Towards Backdoor Attacks in 3D Point Cloud
We present the backdoor attacks in 3D point cloud with a unified framework that exploits the unique properties of 3D data and networks.
HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks
Most existing solutions embed backdoors in DNN model training such that DNN ownership can be verified by triggering distinguishable model behaviors with a set of secret inputs.
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
Ranked #4 on
TDC ADMET Benchmarking Group
on tdcommons