Search Results for author:
Found 25 papers, 6 papers with code
Entity Alignment with Unlabeled Dangling Cases
We investigate the entity alignment problem with unlabeled dangling cases, meaning that there are entities in the source or target graph having no counterparts in the other, and those entities remain unlabeled.
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt
Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new classes with scarce samples while preserving knowledge of old ones.
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection
In the first stage, we devise a scoring network to evaluate the difficulty of training instructions, which is co-trained with the VLM.
SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition
Existing speech language models typically utilize task-dependent prompt tokens to unify various speech tasks in a single model.
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset
This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-context learning to enhance diagnostic processes.
DiPmark: A Stealthy, Efficient and Resilient Watermark for Large Language Models
Watermarking techniques offer a promising way to secure data via embedding covert information into the data.
Shielding the Unseen: Privacy Protection through Poisoning NeRF with Spatial Deformation
In this paper, we introduce an innovative method of safeguarding user privacy against the generative capabilities of Neural Radiance Fields (NeRF) models.
Markov Chain-Guided Graph Construction and Sampling Depth Optimization for EEG-Based Mental Disorder Detection
Graph Neural Networks (GNNs) have received considerable attention since its introduction.
Characterizing normal perinatal development of the human brain structural connectivity
The new computational method and results are useful for assessing normal and abnormal development of the structural connectome early in life.
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets
We identify a phenomenon named player domination in the bargaining game, namely that the existing max-based approaches, such as MAX and MSD, do not converge.
ComedicSpeech: Text To Speech For Stand-up Comedies in Low-Resource Scenarios
Considering comedians have diverse personal speech styles, including personal prosody, rhythm, and fillers, it requires real-world datasets and strong speech style modeling capabilities, which brings challenges.
ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples.
Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks
In this paper, we extensively study this phenomenon for graph data.
VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing
In this paper, we propose a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech.
PromptTTS: Controllable Text-to-Speech with Text Descriptions
Thus, we develop a text-to-speech (TTS) system (dubbed as PromptTTS) that takes a prompt with both style and content descriptions as input to synthesize the corresponding speech.
Towards Robust Dataset Learning
In this paper, we study the problem of learning a robust dataset such that any classifier naturally trained on the dataset is adversarially robust.
Semantic scene descriptions as an objective of human vision
Interpreting the meaning of a visual scene requires not only identification of its constituent objects, but also a rich semantic characterization of object interrelations.
Schizophrenia detection based on EEG using Recurrent Auto-Encoder framework
This study demonstrated that the structure of RAE is able to capture the differential features between SZ patients and HC subjects.
Self-supervised Context-aware Style Representation for Expressive Speech Synthesis
In this paper, we propose a novel framework for learning style representation from abundant plain text in a self-supervised manner.
RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval
The challenge is to design a provably robust algorithm that takes into consideration the 1-NN search and the high-dimensional nature of the embedding space.
AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarios
We model the speaker characteristics systematically to improve the generalization on new speakers.
A Law of Robustness beyond Isoperimetry
We prove a Lipschitzness lower bound $\Omega(\sqrt{n/p})$ of the interpolating neural network with $p$ parameters on arbitrary data distributions.
Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition
In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field.
Understanding Metric Learning on Unit Hypersphere and Generating Better Examples for Adversarial Training
In this paper, we boost the performance of deep metric learning (DML) models with adversarial examples generated by attacking two new objective functions: \textit{intra-class alignment} and \textit{hyperspherical uniformity}.
NeuroGen: activation optimized image synthesis for discovery neuroscience
NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation.
