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Found 20 papers, 9 papers with code
Autonomous self-evolving research on biomedical data: the DREAM paradigm
Utilizing a clinical dataset and two omics datasets, DREAM demonstrated its ability to raise and deepen scientific questions, with difficulty scores for clinical data questions surpassing top published articles by 5. 7% and outperforming GPT-4 and bioinformatics graduate students by 58. 6% and 56. 0%, respectively.
Investigating Decoder-only Large Language Models for Speech-to-text Translation
Large language models (LLMs), known for their exceptional reasoning capabilities, generalizability, and fluency across diverse domains, present a promising avenue for enhancing speech-related tasks.
Snakes and Ladders: Two Steps Up for VideoMamba
Even without extensive pre-training, our models present an increasingly attractive and efficient alternative to current transformer models.
MMCL: Boosting Deformable DETR-Based Detectors with Multi-Class Min-Margin Contrastive Learning for Superior Prohibited Item Detection
Prohibited Item detection in X-ray images is one of the most effective security inspection methods. However, differing from natural light images, the unique overlapping phenomena in X-ray images lead to the coupling of foreground and background features, thereby lowering the accuracy of general object detectors. Therefore, we propose a Multi-Class Min-Margin Contrastive Learning (MMCL) method that, by clarifying the category semantic information of content queries under the deformable DETR architecture, aids the model in extracting specific category foreground information from coupled features. Specifically, after grouping content queries by the number of categories, we employ the Multi-Class Inter-Class Exclusion (MIE) loss to push apart content queries from different groups.
Enhancing Video Transformers for Action Understanding with VLM-aided Training
The FTP framework adds four feature processors that focus on specific aspects of human action in videos: action category, action components, action description, and context information.
Ranked #1 on
Action Recognition
on UCF101
MSLM-S2ST: A Multitask Speech Language Model for Textless Speech-to-Speech Translation with Speaker Style Preservation
There have been emerging research interest and advances in speech-to-speech translation (S2ST), translating utterances from one language to another.
An Empirical Study of Speech Language Models for Prompt-Conditioned Speech Synthesis
Speech language models (LMs) are promising for high-quality speech synthesis through in-context learning.
TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions
A key challenge in continuous sign language recognition (CSLR) is to efficiently capture long-range spatial interactions over time from the video input.
Olapa-MCoT: Enhancing the Chinese Mathematical Reasoning Capability of LLMs
CoT (Chain-of-Thought) is a way to solve reasoning problems for LLMs .
Compensation Sampling for Improved Convergence in Diffusion Models
We argue that the denoising process is crucially limited by an accumulation of the reconstruction error due to an initial inaccurate reconstruction of the target data.
Ranked #3 on
Image Generation
on CelebA 64x64
mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs
In this paper, we present mLoRA, a parallelism-efficient fine-tuning system designed for training multiple LoRA across GPUs and machines.
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction
In this study, we investigate in-context learning (ICL) in document-level event argument extraction (EAE) to alleviate the dependency on large-scale labeled data for this task.
Efficient Temporal Sentence Grounding in Videos with Multi-Teacher Knowledge Distillation
To tackle this problem, we propose a novel efficient multi-teacher model (EMTM) based on knowledge distillation to transfer diverse knowledge from both heterogeneous and isomorphic networks.
DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud
To overcome them, we introduce DLRover-RM, an elastic training framework for DLRMs designed to increase resource utilization and handle the instability of a cloud environment.
Private Multiparty Perception for Navigation
We introduce a framework for navigating through cluttered environments by connecting multiple cameras together while simultaneously preserving privacy.
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations
Moreover, we optimize the training strategy by leveraging more audio to learn MSMCRs better for low-resource languages.
Disentangled Speech Representation Learning for One-Shot Cross-lingual Voice Conversion Using $β$-VAE
We propose an unsupervised learning method to disentangle speech into content representation and speaker identity representation.
Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation
The primary task of ASA fine-tunes the SE with the speech of the target dysarthric speaker to effectively capture identity-related information, and the secondary task applies adversarial training to avoid the incorporation of abnormal speaking patterns into the reconstructed speech, by regularizing the distribution of reconstructed speech to be close to that of reference speech with high quality.
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow
State-of-the-art imitation learning (IL) approaches, e. g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse.
Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks
This argument motivates the current work that presents a novel, channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a channel-wise gating mechanism in the connection between feature groups.
