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Found 178 papers, 68 papers with code
HCRS: A hybrid clothes recommender system based on user ratings and product features
Nowadays, online clothes-selling business has become popular and extremely attractive because of its convenience and cheap-and-fine price.
Fusing Subcategory Probabilities for Texture Classification
Texture, as a fundamental characteristic of objects, has attracted much attention in computer vision research.
Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator.
T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction
Precise destination prediction of taxi trajectories can benefit many intelligent location based services such as accurate ad for passengers.
Multiple VLAD encoding of CNNs for image classification
In this paper, we propose a special framework, which is the multiple VLAD encoding method with the CNNs features for image classification.
A Network Framework for Noisy Label Aggregation in Social Media
This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents.
Cultural Vocal Bursts Intensity Prediction
Image Classification
+2
AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-domain images.
Locally-Transferred Fisher Vectors for Texture Classification
Texture classification has been extensively studied in computer vision.
On the Effectiveness of Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss for both the discriminator and the generator.
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions
Most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer.
VizWiz Grand Challenge: Answering Visual Questions from Blind People
The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings.
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting.
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions
Most existing works in visual question answering (VQA) are dedicated to improving the accuracy of predicted answers, while disregarding the explanations.
Ranked #4 on
Explanatory Visual Question Answering
on GQA-REX
Explanatory Visual Question Answering
Multi-Task Learning
+1
Unpaired Multi-Domain Image Generation via Regularized Conditional GANs
To tackle this problem, we propose Regularized Conditional GAN (RegCGAN) which is capable of learning to generate corresponding images in the absence of paired training data.
Single Bitmap Block Truncation Coding of Color Images Using Hill Climbing Algorithm
Compared with various schemes, the simulation results of the proposed scheme are better than that of the reference schemes in visual quality and time consumption.
An Investigation of Few-Shot Learning in Spoken Term Classification
In this paper, we investigate the feasibility of applying few-shot learning algorithms to a speech task.
Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization
6DOF camera relocalization is an important component of autonomous driving and navigation.
Learning Shared Semantic Space with Correlation Alignment for Cross-modal Event Retrieval
In this paper, we propose to learn shared semantic space with correlation alignment (${S}^{3}CA$) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data.
Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network
With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data.
Graph Neural Networks for Social Recommendation
These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.
Ranked #3 on
Recommendation Systems
on Epinions
(using extra training data)
pyLLE: a Fast and User Friendly Lugiato-Lefever Equation Solver
We present the development of pyLLE, a freely accessible and cross-platform Lugiato-Lefever equation solver programmed in Python and Julia and optimized for the simulation of microresonator frequency combs.
Mathematical Software Optics
MMED: A Multi-domain and Multi-modality Event Dataset
In this work, we construct and release a multi-domain and multi-modality event dataset (MMED), containing 25, 165 textual news articles collected from hundreds of news media sites (e. g., Yahoo News, Google News, CNN News.)
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation.
Virtual Mixup Training for Unsupervised Domain Adaptation
Existing methods only impose the locally-Lipschitz constraint around the training points while miss the other areas, such as the points in-between training data.
Deep Adversarial Social Recommendation
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life.
Deep Social Collaborative Filtering
Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations.
Product Image Recognition with Guidance Learning and Noisy Supervision
Instead of collecting product images by labor-and time-intensive image capturing, we take advantage of the web and download images from the reviews of several e-commerce websites where the images are casually captured by consumers.
Why Does a Visual Question Have Different Answers?
Visual question answering is the task of returning the answer to a question about an image.
Learning Category Correlations for Multi-label Image Recognition with Graph Networks
In this paper, instead of using a pre-defined graph which is inflexible and may be sub-optimal for multi-label classification, we propose the A-GCN, which leverages the popular Graph Convolutional Networks with an Adaptive label correlation graph to model label dependencies.
Effective Stabilized Self-Training on Few-Labeled Graph Data
However, under extreme cases when very few labels are available (e. g., 1 labeled node per class), GNNs suffer from severe performance degradation.
Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling
Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features.
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks
More importantly, for a cold start user/item that does not have any interactions, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph.
Incorporating Effective Global Information via Adaptive Gate Attention for Text Classification
The dominant text classification studies focus on training classifiers using textual instances only or introducing external knowledge (e. g., hand-craft features and domain expert knowledge).
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
In this paper, we address these issues and close the loop of neural-symbolic learning by (1) introducing the \textbf{grammar} model as a \textit{symbolic prior} to bridge neural perception and symbolic reasoning, and (2) proposing a novel \textbf{back-search} algorithm which mimics the top-down human-like learning procedure to propagate the error through the symbolic reasoning module efficiently.
PoseGAN: A Pose-to-Image Translation Framework for Camera Localization
We present PoseGANs, a conditional generative adversarial networks (cGANs) based framework for the implementation of pose-to-image translation.
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation
To the best of our knowledge, this work is the first extremely lightweight neural network trained on monocular video sequences for real-time unsupervised monocular depth estimation, which opens up the possibility of implementing deep learning-based real-time unsupervised monocular depth prediction on low-cost embedded devices.
Ranked #5 on
Semantic Segmentation
on SpectralWaste
Neural Mixed Counting Models for Dispersed Topic Discovery
Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics.
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering
Visual question answering aims to answer the natural language question about a given image.
A Competence-aware Curriculum for Visual Concepts Learning via Question Answering
Humans can progressively learn visual concepts from easy to hard questions.
Physical properties revealed by transport measurements on superconducting Nd$_{0.8}$Sr$_{0.2}$NiO$_{2}$ thin films
The angle dependent resistivity at a fixed temperature and different magnetic fields cannot be scaled to one curve, which deviates from the prediction of the anisotropic Ginzburg-Landau theory.
Superconductivity Materials Science Strongly Correlated Electrons
Object-aware Multimodal Named Entity Recognition in Social Media Posts with Adversarial Learning
To better exploit visual and textual information in NER, we propose an adversarial gated bilinear attention neural network (AGBAN).
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization
MetaMix can be integrated with any of the MAML-based algorithms and learn the decision boundaries generalizing better to new tasks.
A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction
Tailings ponds are places for storing industrial waste.
Suppressing Mislabeled Data via Grouping and Self-Attention
Specifically, this plug-and-play AFM first leverages a \textit{group-to-attend} module to construct groups and assign attention weights for group-wise samples, and then uses a \textit{mixup} module with the attention weights to interpolate massive noisy-suppressed samples.
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications
Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions.
Controllable Abstractive Sentence Summarization with Guiding Entities
Entities are the major proportion and build up the topic of text summaries.
A Unified Sequence Labeling Model for Emotion Cause Pair Extraction
Existing approaches address the task by first extracting emotion and cause clauses via two binary classifiers separately, and then training another binary classifier to pair them up.
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification
Relation Classification (RC) plays an important role in natural language processing (NLP).
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition
To appropriately describe the relations between joints in the skeleton graph, we propose a multi-scale graph strategy, adopting a full-scale graph, part-scale graph, and core-scale graph to capture the local features of each joint and the contour features of important joints.
Learning by Fixing: Solving Math Word Problems with Weak Supervision
To generate more diverse solutions, \textit{tree regularization} is applied to guide the efficient shrinkage and exploration of the solution space, and a \textit{memory buffer} is designed to track and save the discovered various fixes for each problem.
Ranked #1 on
Math Word Problem Solving
on Math23K
(weakly-supervised metric)
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar
Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability.
Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc.
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics
We believe the HINT dataset and the experimental findings are of great interest to the learning community on systematic generalization.
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering
The multi-label learning module leverages a memory feature bank and assigns each image with a multi-label vector based on the similarities between the image and feature bank.
VLGrammar: Grounded Grammar Induction of Vision and Language
In this work, we study grounded grammar induction of vision and language in a joint learning framework.
Intent Disentanglement and Feature Self-supervision for Novel Recommendation
Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems.
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes
In this paper, we introduce a multiple-criteria based active learning algorithm, which incorporates three complementary criteria, i. e., informativeness, representativeness and diversity, to make appropriate selections in the active learning rounds under different data types.
Adversarial Learning with Mask Reconstruction for Text-Guided Image Inpainting
In this paper, we propose an adversarial learning framework with mask reconstruction (ALMR) for image inpainting with textual guidance, which consists of a two-stage generator and dual discriminators.
Jointly Attacking Graph Neural Network and its Explanations
Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.
DGEM: A New Dual-modal Graph Embedding Method in Recommendation System
In the current deep learning based recommendation system, the embedding method is generally employed to complete the conversion from the high-dimensional sparse feature vector to the low-dimensional dense feature vector.
Graph Trend Filtering Networks for Recommendations
The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.
YouRefIt: Embodied Reference Understanding with Language and Gesture
To the best of our knowledge, this is the first embodied reference dataset that allows us to study referring expressions in daily physical scenes to understand referential behavior, human communication, and human-robot interaction.
Stabilized Self-training with Negative Sampling on Few-labeled Graph Data
Therefore, we propose an effective framework, Stabilized self-training with Negative sampling (SN), which is applicable to existing GNNs to stabilize the training process and enhance the training data, and consequently, boost classification accuracy on graphs with few labeled data.
Imperceptible Black-box Attack via Refining in Salient Region
To address this issue, in this paper we propose to use segmentation priors for black-box attacks such that the perturbations are limited in the salient region.
DM-CT: Consistency Training with Data and Model Perturbation
The common approach of consistency training is performed on the data-level, which typically utilizes the data augmentation strategy (or adversarial training) to make the predictions from the augmented input and the original input to be consistent, so that the model is more robust and attains better generalization ability.
Multi-View Self-Attention Based Transformer for Speaker Recognition
In this work, we propose a novel multi-view self-attention mechanism and present an empirical study of different Transformer variants with or without the proposed attention mechanism for speaker recognition.
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Benchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve Optimization
Evolutionary transfer multiobjective optimization (ETMO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others.
Towards General Deep Leakage in Federated Learning
We find that image restoration fails even if there is only one incorrectly inferred label in the batch; we also find that when batch images have the same label, the corresponding image is restored as a fusion of that class of images.
Deep Keyphrase Completion
Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval.
Self-Ensemling for 3D Point Cloud Domain Adaption
In SEN, a student network is kept in a collaborative manner with supervised learning and self-supervised learning, and a teacher network conducts temporal consistency to learn useful representations and ensure the quality of point clouds reconstruction.
Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text
The experiments show that the resultant unified foundation transformer works surprisingly well on both the vision-only and text-only tasks, and the proposed knowledge distillation and gradient masking strategy can effectively lift the performance to approach the level of separately-trained models.
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks
To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism.
Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks
Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.
Conceptor Learning for Class Activation Mapping
Class Activation Mapping (CAM) has been widely adopted to generate saliency maps which provides visual explanations for deep neural networks (DNNs).
Indicative Image Retrieval: Turning Blackbox Learning into Grey
In many applications, we need the matching evidence to be indicated rather than just have the ranked list (e. g., the locations of the target proteins/cells/lesions in medical images).
Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks
This study for the first time considers both types of risk and their joint effects in bankruptcy prediction.
An End-to-End Cascaded Image Deraining and Object Detection Neural Network
Firstly, the gap between the low-level vision task represented by rain removal and the high-level vision task represented by object detection is significant, and the low-level vision task can hardly contribute to the high-level vision task.
Towards Sustainable Satellite Edge Computing
Recently, Low Earth Orbit (LEO) satellites experience rapid development and satellite edge computing emerges to address the limitation of bent-pipe architecture in existing satellite systems.
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methods
Actor-critic Reinforcement Learning (RL) algorithms have achieved impressive performance in continuous control tasks.
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack
In this paper, we propose to restrict the perturbations to a small salient region to generate adversarial examples that can hardly be perceived.
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios
In this paper, we propose a sampling scheme, Monte-Carlo Pareto Optimization for Active Learning (POAL), which selects optimal subsets of unlabeled samples with fixed batch size from the unlabeled data pool.
Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability
The recently proposed pivotal tuning model makes significant progress towards reconstruction and editability, by using a two-step approach that first inverts the input image into a latent code, called pivot code, and then alters the generator so that the input image can be accurately mapped into the pivot code.
Knowledge-enhanced Black-box Attacks for Recommendations
Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i. e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items.
InitialGAN: A Language GAN with Completely Random Initialization
Text generative models trained via Maximum Likelihood Estimation (MLE) suffer from the notorious exposure bias problem, and Generative Adversarial Networks (GANs) are shown to have potential to tackle this problem.
IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression
Point cloud is a crucial representation of 3D contents, which has been widely used in many areas such as virtual reality, mixed reality, autonomous driving, etc.
Disentangled Contrastive Learning for Social Recommendation
In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec.
A Comprehensive Survey on Trustworthy Recommender Systems
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.
Fairness Reprogramming
Specifically, FairReprogram considers the case where models can not be changed and appends to the input a set of perturbations, called the fairness trigger, which is tuned towards the fairness criteria under a min-max formulation.
Neural-Symbolic Recursive Machine for Systematic Generalization
In experiments, NSR achieves state-of-the-art performance in three benchmarks from different domains: SCAN for semantic parsing, PCFG for string manipulation, and HINT for arithmetic reasoning.
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
To address these issues, we introduce hyper surface fitting to implicitly learn hyper surfaces, which are represented by multi-layer perceptron (MLP) layers that take point features as input and output surface patterns in a high dimensional feature space.
Ranked #2 on
Surface Normals Estimation
on PCPNet
SQA3D: Situated Question Answering in 3D Scenes
We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D).
Ranked #1 on
Referring Expression
on SQA3D
Scene-Text Oriented Reffering Expression Comprehension
Abstract—Referring expression comprehension (REC) aims to identify and locate a specific object in visual scenes referred to by a natural language expression.
ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling
We address the problem by introducing a novel joint method on top of BERT which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby contributing to the two tasks.
A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective
In contrast, this paper attempts to provide a systematic literature survey of enterprise risk analysis approaches from Big Data perspective, which reviews more than 250 representative articles in the past almost 50 years (from 1968 to 2023).
Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation
Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding.
Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging
This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction task (JMERE).
Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization
Designing an intelligent volume-weighted average price (VWAP) strategy is a critical concern for brokers, since traditional rule-based strategies are relatively static that cannot achieve a lower transaction cost in a dynamic market.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey
Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.
AttMEMO : Accelerating Transformers with Memoization on Big Memory Systems
Transformer models gain popularity because of their superior inference accuracy and inference throughput.
Generative Diffusion Models on Graphs: Methods and Applications
Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.
Fairly Adaptive Negative Sampling for Recommendations
Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i. e., clicked by a user) and negative items (i. e., obtained by negative sampling).
Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction
Accurate and real-time traffic state prediction is of great practical importance for urban traffic control and web mapping services.
Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting
The key challenge is to effectively model complex spatial-temporal dependencies and correlations in modern traffic data.
MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation
Representing medical concepts for healthcare analytical tasks requires incorporating medical domain knowledge and prior information from patient description data.
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset.
An Adversarial Non-Autoregressive Model for Text Generation with Incomplete Information
Non-autoregressive models have been widely studied in the Complete Information Scenario (CIS), in which the input has complete information of corresponding output.
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models.
SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds
In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner.
Inferring Attracting Basins of Power System with Machine Learning
Power systems dominated by renewable energy encounter frequently large, random disturbances, and a critical challenge faced in power-system management is how to anticipate accurately whether the perturbed systems will return to the functional state after the transient or collapse.
FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design
By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design.
Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective
Molecule discovery plays a crucial role in various scientific fields, advancing the design of tailored materials and drugs.
Ranked #3 on
Text-based de novo Molecule Generation
on ChEBI-20
Recurrent Attention Networks for Long-text Modeling
Revisiting the self-attention mechanism and the recurrent structure, this paper proposes a novel long-document encoding model, Recurrent Attention Network (RAN), to enable the recurrent operation of self-attention.
Farthest Streamline Sampling for the Uniform Distribution of Forearm Muscle Fiber Tracts from Diffusion Tensor Imaging
FSS reduced the sampling of long tracts (10% reduction in fiber length, P<0. 05), and the architectural parameters were within physiological ranges (two parameters with P<0. 05).
STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction
However, a survey study of graph learning, spatial-temporal graph models for traffic, as well as a fair comparison of baseline models are pending and unavoidable issues.
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition.
Recommender Systems in the Era of Large Language Models (LLMs)
As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems.
Counterfactual Explanation for Fairness in Recommendation
The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes.
Certified Robustness for Large Language Models with Self-Denoising
This largely falls into the study of certified robust LLMs, i. e., all predictions of LLM are certified to be correct in a local region around the input.
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies.
DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation
The Class Incremental Semantic Segmentation (CISS) extends the traditional segmentation task by incrementally learning newly added classes.
3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment
3D vision-language grounding (3D-VL) is an emerging field that aims to connect the 3D physical world with natural language, which is crucial for achieving embodied intelligence.
LLaMA-E: Empowering E-commerce Authoring with Multi-Aspect Instruction Following
E-commerce authoring involves creating attractive, abundant, and targeted promotional content to drive product sales.
Consensus-based Distributed Variational Multi-object Tracker in Multi-Sensor Network
Then, we develop a distributed version leveraging the average consensus algorithm, which is theoretically equivalent to the centralised sensor fusion tracker and requires only local message passing with neighbouring sensors.
Effective Multi-Graph Neural Networks for Illicit Account Detection on Cryptocurrency Transaction Networks
Extensive experiments, comparing against 14 existing solutions on 4 large cryptocurrency datasets of Bitcoin and Ethereum, demonstrate that DIAM consistently achieves the best performance to accurately detect illicit accounts, while being efficient.
Variational Tracking and Redetection for Closely-spaced Objects in Heavy Clutter
The non-homogeneous Poisson process (NHPP) is a widely used measurement model that allows for an object to generate multiple measurements over time.
Neural Gradient Learning and Optimization for Oriented Point Normal Estimation
We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation.
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
These issues compromise both the accuracy and the computational efficiency of models in both Machine Learning and Unlearning.
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
To model the compatibility between user intents and item properties, we design the user-item co-clustering module, maximizing the mutual information of co-clusters of users and items.
Label Supervised LLaMA Finetuning
We evaluate this approach with Label Supervised LLaMA (LS-LLaMA), based on LLaMA-2-7B, a relatively small-scale LLM, and can be finetuned on a single GeForce RTX4090 GPU.
Ranked #1 on
Named Entity Recognition (NER)
on CoNLL03
(F1 (micro) metric)
Dataset Condensation for Recommendation
However, applying existing approaches to condense recommendation datasets is impractical due to following challenges: (i) sampling-based methods are inadequate in addressing the long-tailed distribution problem; (ii) synthesizing-based methods are not applicable due to discreteness of interactions and large size of recommendation datasets; (iii) neither of them fail to address the specific issue in recommendation of false negative items, where items with potential user interest are incorrectly sampled as negatives owing to insufficient exposure.
Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow Prediction
Traffic flow prediction is one of the most fundamental tasks of intelligent transportation systems.
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Notably, the condensation paradigm of this method is forward and free from iterative optimization on the synthesized dataset.
Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World
We even conceived a neuro-symbolic reasoning approach that reconciles LLMs & VLMs with logical reasoning to emulate the human problem-solving process for Bongard Problems.
Ranked #1 on
Visual Reasoning
on Bongard-OpenWorld
Fast Graph Condensation with Structure-based Neural Tangent Kernel
The rapid development of Internet technology has given rise to a vast amount of graph-structured data.
Embedding in Recommender Systems: A Survey
This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
Specifically, we introduce loss functions to facilitate query points to iteratively reach the moving targets and aggregate onto the approximated surface, thereby learning a global surface representation of the data.
Untargeted Black-box Attacks for Social Recommendations
To perform untargeted attacks on social recommender systems, attackers can construct malicious social relationships for fake users to enhance the attack performance.
An Embodied Generalist Agent in 3D World
Leveraging massive knowledge and learning schemes from large language models (LLMs), recent machine learning models show notable successes in building generalist agents that exhibit the capability of general-purpose task solving in diverse domains, including natural language processing, computer vision, and robotics.
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions
To highlight the context in such special causal relationships, we propose a new task to determine whether or not an input pair of emotion and cause has a valid causal relationship under different contexts and extract the specific context clauses that participate in the causal relationship.
Compositional Inversion for Stable Diffusion Models
It stems from the fact that during inversion, the irrelevant semantics in the user images are also encoded, forcing the inverted concepts to occupy locations far from the core distribution in the embedding space.
CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update
However, these methods often overlook the potential for continual learning, typically by freezing the utilized tools, thus limiting their adaptation to environments requiring new knowledge.
Cross Initialization for Personalized Text-to-Image Generation
Additionally, a fast version of our method allows for capturing an input image in roughly 26 seconds, while surpassing the baseline methods in terms of both reconstruction and editability.
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models
Experiments show that QST can reduce the total memory footprint by up to 2. 3 $\times$ and speed up the finetuning process by up to 3 $\times$ while achieving competent performance compared with the state-of-the-art.
3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework
Comparative evaluations with popular point-based methods on HPoint103 and the public dataset DHP19 demonstrate the dramatic outperformance of our D-CPT.
Developing ChatGPT for Biology and Medicine: A Complete Review of Biomedical Question Answering
Central to our focus is the utilizing of language models and multimodal paradigms for medical question answering, aiming to guide the research community in selecting appropriate mechanisms for their specific medical research requirements.
SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding
In comparison to recent advancements in the 2D domain, grounding language in 3D scenes faces several significant challenges: (i) the inherent complexity of 3D scenes due to the diverse object configurations, their rich attributes, and intricate relationships; (ii) the scarcity of paired 3D vision-language data to support grounded learning; and (iii) the absence of a unified learning framework to distill knowledge from grounded 3D data.
PSAvatar: A Point-based Morphable Shape Model for Real-Time Head Avatar Animation with 3D Gaussian Splatting
In this paper, we introduce PSAvatar, a novel framework for animatable head avatar creation that utilizes discrete geometric primitive to create a parametric morphable shape model and employs 3D Gaussian for fine detail representation and high fidelity rendering.
3D Reconstruction and New View Synthesis of Indoor Environments based on a Dual Neural Radiance Field
One of the innovative features of Du-NeRF is that it decouples a view-independent component from the density field and uses it as a label to supervise the learning process of the SDF field.
One-Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization Training
During the gradient descent learning, a one-step forward search is designed to find the trial gradient of the next-step, which is adopted to adjust the gradient of current step towards the direction of fast convergence.
Segment Anything in 3D Gaussians
In this paper, we propose a novel approach to achieve object segmentation in 3D Gaussian via an interactive procedure without any training process and learned parameters.
Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey
Large-scale pre-trained vision models (PVMs) have shown great potential for adaptability across various downstream vision tasks.
Progress and Opportunities of Foundation Models in Bioinformatics
Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs.
OV-NeRF: Open-vocabulary Neural Radiance Fields with Vision and Language Foundation Models for 3D Semantic Understanding
First, from the single-view perspective, we introduce Region Semantic Ranking (RSR) regularization by leveraging 2D mask proposals derived from SAM to rectify the noisy semantics of each training view, facilitating accurate semantic field learning.
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning.
Linear-Time Graph Neural Networks for Scalable Recommendations
In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs' powerful expressiveness for superior prediction accuracy.
2D Matryoshka Sentence Embeddings
The experimental results demonstrate the effectiveness of our proposed model in dynamically supporting different embedding sizes and Transformer layers, allowing it to be highly adaptable to various scenarios.
HoloVIC: Large-scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative
Based on HoloVIC, we formulated four tasks to facilitate the development of related research.
Large Language Models are In-Context Molecule Learners
Specifically, ICMA incorporates the following three stages: Hybrid Context Retrieval, Post-retrieval Re-ranking, and In-context Molecule Tuning.
FashionReGen: LLM-Empowered Fashion Report Generation
Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports.
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video understanding problem, especially capturing the long-term temporal relations in lengthy videos.
INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations
In this paper, we present a framework that is capable of learning structured states and symbolic policies simultaneously, whose key idea is to overcome the efficiency bottleneck by distilling vision foundation models into a scalable perception module.
Generative Active Learning for Image Synthesis Personalization
The primary challenge in conducting active learning on generative models lies in the open-ended nature of querying, which differs from the closed form of querying in discriminative models that typically target a single concept.
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting
Open-vocabulary 3D scene understanding presents a significant challenge in computer vision, withwide-ranging applications in embodied agents and augmented reality systems.
A Picture Is Worth a Graph: Blueprint Debate on Graph for Multimodal Reasoning
The study addresses two key challenges: the trivialization of opinions resulting from excessive summarization and the diversion of focus caused by distractor concepts introduced from images.
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Despite tremendous advancements in large language models (LLMs) over recent years, a notably urgent challenge for their practical deployment is the phenomenon of hallucination, where the model fabricates facts and produces non-factual statements.
Automated discovery of symbolic laws governing skill acquisition from naturally occurring data
Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes.
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerable concerns.
Exploring Non-Autoregressive Text Style Transfer
In this paper, we explore Non-AutoRegressive (NAR) decoding for unsupervised text style transfer.
Conditional Causal Relationships between Emotions and Causes in Texts
The causal relationships between emotions and causes in text have recently received a lot of attention.
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer
In this paper, we propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders, one decoding from left to right while the other decoding from right to left.
Task-oriented Domain-specific Meta-Embedding for Text Classification
Meta-embedding learning, which combines complementary information in different word embeddings, have shown superior performances across different Natural Language Processing tasks.
