Search Results for author:
Found 21 papers, 7 papers with code
Outcome-supervised Verifiers for Planning in Mathematical Reasoning
These findings offer a novel perspective on the role of outcome supervision in training verifiers for multi-step reasoning tasks and provide theoretical justification for its advantage in value estimation for planning.
Ranked #16 on
Arithmetic Reasoning
on GSM8K
A Better Match for Drivers and Riders: Reinforcement Learning at Lyft
This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time.
Data-Centric Financial Large Language Models
Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.
AceGPT, Localizing Large Language Models in Arabic
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
AGS: An Dataset and Taxonomy for Domestic Scene Sound Event Recognition
In particular, there is no public common data set for the research field of sound event recognition for the data set of the indoor environmental sound scene.
A Huber Loss Minimization Approach to Byzantine Robust Federated Learning
Federated learning systems are susceptible to adversarial attacks.
Stochastic Step-wise Feature Selection for Exponential Random Graph Models (ERGMs)
Statistical analysis of social networks provides valuable insights into complex network interactions across various scientific disciplines.
MODA: Mapping-Once Audio-driven Portrait Animation with Dual Attentions
Audio-driven portrait animation aims to synthesize portrait videos that are conditioned by given audio.
HuatuoGPT, towards Taming Language Model to Be a Doctor
Experimental results demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets.
Phoenix: Democratizing ChatGPT across Languages
This paper presents our efforts to democratize ChatGPT across language.
Natural Language Reasoning, A Survey
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically.
Accurate 3D Face Reconstruction with Facial Component Tokens
However, the current deep learning-based methods face significant challenges in achieving accurate reconstruction with disentangled facial parameters and ensuring temporal stability in single-frame methods for 3D face tracking on video data.
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Therefore, we propose a method called region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning.
Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training
This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training.
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation
Blood vessel segmentation is crucial for many diagnostic and research applications.
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph
Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language.
Ranked #2 on
Visual Question Answering (VQA)
on VCR (QA-R) test
Referring Expression Comprehension
Visual Question Answering (VQA)
Attentive Geo-Social Group Recommendation
In this paper, a novel group recommendation method, called attentive geo-social group recommendation, is proposed to recommend the target user with both activity locations and a group of users that may join the activities.
PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation
The experimental results show that the PGU-net+ has superior accuracy than the previous state-of-the-art methods on cervical nuclei segmentation.
Multi-level Domain Adaptive learning for Cross-Domain Detection
In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment.
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible.
Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases
Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way.
