Search Results for author:
Found 59 papers, 23 papers with code
Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clustering
To address this challenge, we introduce the nonlinear Causal Kernel Clustering method designed for heterogeneous subgroup causal learning, illuminating variations in causal relationships across diverse subgroups.
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training
Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks, yet their training remains highly resource-intensive and susceptible to critical challenges such as training instability.
FineVQ: Fine-Grained User Generated Content Video Quality Assessment
The rapid growth of user-generated content (UGC) videos has produced an urgent need for effective video quality assessment (VQA) algorithms to monitor video quality and guide optimization and recommendation procedures.
F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration
The FaceQ database comprises 12, 255 images generated by 29 models across three tasks: (1) face generation, (2) face customization, and (3) face restoration.
Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Models
In both applications, our method outperforms both conventional neural network based approaches as-well as state-of-the-art Physics Informed Machine Learning methods.
Symbol Level Precoding for Systems with Improper Gaussian Interference
This paper focuses on precoding design in multi-antenna systems with improper Gaussian interference (IGI), characterized by correlated real and imaginary parts.
LegiLM: A Fine-Tuned Legal Language Model for Data Compliance
Ensuring compliance with international data protection standards for privacy and data security is a crucial but complex task, often requiring substantial legal expertise.
An Empirical Study on Information Extraction using Large Language Models
To demonstrate the latest representative progress in LLMs' information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types.
Event-Triggered Robust Cooperative Output Regulation for a Class of Linear Multi-Agent Systems with an Unknown Exosystem
This paper investigates the robust cooperative output regulation problem for a class of heterogeneous uncertain linear multi-agent systems with an unknown exosystem via event-triggered control (ETC).
Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters
In our solution, a deep learning model was first trained to retarget the facial expression from input face images to virtual human faces by estimating the blendshape coefficients.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Visual Question Answering (VQA)
on InfographicVQA
Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation
An optimized scenario generation toolkit is developed within BridgeGen.
Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN
In a recent paper, we introduced a \emph{worst-case} upper bound on how challenging morphing attacks can be for an FR system.
MRecGen: Multimodal Appropriate Reaction Generator
Verbal and non-verbal human reaction generation is a challenging task, as different reactions could be appropriate for responding to the same behaviour.
Reversible Graph Neural Network-based Reaction Distribution Learning for Multiple Appropriate Facial Reactions Generation
This means that we approach this problem by considering the generation of a distribution of the listener's appropriate facial reactions instead of multiple different appropriate facial reactions, i. e., 'many' appropriate facial reaction labels are summarised as 'one' distribution label during training.
An Empirical Study on Information Extraction using Large Language Models
To demonstrate the latest representative progress in LLMs' information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types.
One-Bit Massive MIMO Precoding for Frequency-Selective Fading Channels
While block-wise processing (BWP) can effectively address the inter-symbol-interference (ISI) in frequency-selective fading channels, its computational complexity and processing delay can be too high for practical implementation.
Bokeh Rendering Based on Adaptive Depth Calibration Network
This allows the camera to capture images with shallow depth-of-field, in which only a small area of the image is in sharp focus, while the rest of the image is blurred.
Robust Symbol Level Precoding for Overlay Cognitive Radio Networks
This paper focuses on designing robust symbol-level precoding (SLP) in an overlay cognitive radio (CR) network, where the primary and secondary networks transmit signals concurrently.
Document-level Relation Extraction with Relation Correlations
Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
To prevent these issues from hindering the deployment of FL systems, we propose a lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch.
The least-used key selection method for information retrieval in large-scale Cloud-based service repositories
The experimental results show that the proposed least-used key selection method improves the service retrieval efficiency significantly compared with the designated key selection method in the case of the unequal appearing probability of parameters in service retrieval requests under three indexing models.
Distributed Optimal Output Consensus of Uncertain Nonlinear Multi-Agent Systems over Unbalanced Directed Networks via Output Feedback
In this note, a novel observer-based output feedback control approach is proposed to address the distributed optimal output consensus problem of uncertain nonlinear multi-agent systems in the normal form over unbalanced directed graphs.
Recognizing Vector Graphics without Rasterization
In this paper, we consider a different data format for images: vector graphics.
Cloning one's voice using very limited data in the wild
(2) How to clone a person's voice while controlling the style and prosody.
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of global warming and climate change.
Towards robust and domain agnostic reinforcement learning competitions
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception
Based on the above, in this paper, we design a novel Recursive Hierarchy-Interactive Attention network (RHIA) to further handle long-tail relations, which models the heuristic effect between relation levels.
Human Object Interaction Detection using Two-Direction Spatial Enhancement and Exclusive Object Prior
At inference, we propose a human-object regrouping approach by considering the object-exclusive property of an action, where the target object should not be shared by more than one human.
FedProto: Federated Prototype Learning across Heterogeneous Clients
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space.
Isometric Propagation Network for Generalized Zero-shot Learning
To resolve this problem, we propose Isometric Propagation Network (IPN), which learns to strengthen the relation between classes within each space and align the class dependency in the two spaces.
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task
Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks.
Free Lunch for Few-shot Learning: Distribution Calibration
In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples, then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier.
Real-Time AutoML
We train a graph neural network in which each node represents a dataset to predict the best machine learning pipeline for a new test dataset.
MASP: Model-Agnostic Sample Propagation for Few-shot learning
Few-shot learning aims to train a classifier given only a few samples per class that are highly insufficient to describe the whole data distribution.
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering
The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM).
Attribute Propagation Network for Graph Zero-shot Learning
To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes.
Improving Coreference Resolution by Leveraging Entity-Centric Features with Graph Neural Networks and Second-order Inference
One of the major challenges in coreference resolution is how to make use of entity-level features defined over clusters of mentions rather than mention pairs.
NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size
In this paper, we propose NATS-Bench, a unified benchmark on searching for both topology and size, for (almost) any up-to-date NAS algorithm.
Two-dimensional ferromagnetic semiconductor VBr3 with tunable anisotropy
Two-dimensional (2D) ferromagnets (FMs) have attracted widespread attention due to their prospects in spintronic applications.
Materials Science Strongly Correlated Electrons
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings.
A Universal Representation Transformer Layer for Few-Shot Image Classification
We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources.
Ranked #1 on
Few-Shot Image Classification
on Meta-Dataset Rank
Interpretable Time-series Classification on Few-shot Samples
Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples.
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework
A generative network (GN) takes two elements of a (subject, predicate, object) triple as input and generates the vector representation of the missing element.
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
Particularly, it is a set of kernel sizes that can efficiently cover the best RF size across different datasets via consisting of multiple prime numbers according to the length of the time series.
AutoML using Metadata Language Embeddings
We use these embeddings in a neural architecture to learn the distance between best-performing pipelines.
Learning to Propagate for Graph Meta-Learning
It can significantly improve tasks that suffer from insufficient training data, e. g., few shot learning.
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning
With the development of artificial intelligence algorithms like deep learning models and the successful applications in many different fields, further similar trails of deep learning technology have been made in cyber security area.
Generating Responses with a Specific Emotion in Dialog
It is desirable for dialog systems to have capability to express specific emotions during a conversation, which has a direct, quantifiable impact on improvement of their usability and user satisfaction.
Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets
We introduce a system designed to match scholarly document entities with noisy metadata against a reference dataset.
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
The resulting graph of prototypes can be continually re-used and updated for new tasks and classes.
MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy
It addresses the ``many-class'' problem by exploring the class hierarchy, e. g., the coarse-class label that covers a subset of fine classes, which helps to narrow down the candidates for the fine class and is cheaper to obtain.
Certainty Driven Consistency Loss on Multi-Teacher Networks for Semi-Supervised Learning
Specifically, we propose two approaches, i. e. Filtering CCL and Temperature CCL to either filter out uncertain predictions or pay less attention on them in the consistency regularization.
From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generation
We introduce a new task named Story Ending Generation (SEG), whic-h aims at generating a coherent story ending from a sequence of story plot.
Loss Guided Activation for Action Recognition in Still Images
This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features.
BLCU\_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention
To comprehend an argument and fill the gap between claims and reasons, it is vital to find the implicit supporting warrants behind.
Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization
We propose a new model based on the deconvolutional networks and SAX discretization to learn the representation for multivariate time series.
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series
Time series and signals are attracting more attention across statistics, machine learning and pattern recognition as it appears widely in the industry especially in sensor and IoT related research and applications, but few advances has been achieved in effective time series visual analytics and interaction due to its temporal dimensionality and complex dynamics.
