2 citations · 4 across the 12 of their papers we have counts for
12 papers
CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems
Tingkai Liu, Muralidhar Andoorveedu, Sanjoy Das +2
The evolution of compute infrastructure has transformed multi-GPU systems into tightly integrated shared-memory structures. However, current software still mostly treats these cohe…
A cross-species neural foundation model for end-to-end speech decoding
Yizi Zhang, Linyang He, Chaofei Fan +9
Speech brain-computer interfaces (BCIs) aim to restore communication for people with paralysis by translating neural activity into text. Most systems use cascaded frameworks that d…
Token-Level Uncertainty-Aware Objective for Language Model Post-Training
Tingkai Liu, Ari S. Benjamin, Anthony M. Zador
In the current work, we connect token-level uncertainty in causal language modeling to two types of training objectives: 1) masked maximum likelihood (MLE), 2) self-distillation. W…
NdLinear: Preserving Multi-Dimensional Structure for Parameter-Efficient Neural Networks
Alex Reneau, Jerry Yao-Chieh Hu, Zhongfang Zhuang +6
In deep learning, processing multidimensional inputs (e.g., images, medical scans, and time series) is an important task that often requires flattening the inputs. We introduce $\m…
FullStack Bench: Evaluating LLMs as Full Stack Coders
Bytedance-Seed-Foundation-Code-Team, :, Yao Cheng +53
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing. However, most exist…
ElastiFormer: Learned Redundancy Reduction in Transformer via Self-Distillation
Junzhang Liu, Tingkai Liu, Yueyuan Sui +1
We introduce ElastiFormer, a post-training technique that adapts pretrained Transformer models into an elastic counterpart with variable inference time compute. ElastiFormer introd…
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data
Xuwu Wang, Qiwen Cui, Yunzhe Tao +16
Large language models (LLMs) have become increasingly pivotal across various domains, especially in handling complex data types. This includes structured data processing, as exempl…
Let's reward step by step: Step-Level reward model as the Navigators for Reasoning
Qianli Ma, Haotian Zhou, Tingkai Liu +4
Recent years have seen considerable advancements in multi-step reasoning with Large Language Models (LLMs). The previous studies have elucidated the merits of integrating feedback…
Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling
Haogeng Liu, Qihang Fan, Tingkai Liu +5
This paper proposes Video-Teller, a video-language foundation model that leverages multi-modal fusion and fine-grained modality alignment to significantly enhance the video-to-text…
DavIR: Data Selection via Implicit Reward for Large Language Models
Haotian Zhou, Tingkai Liu, Qianli Ma +5
We introduce DavIR, a model-based data selection method for post-training Large Language Models. DavIR generalizes Reducible Holdout Loss to core-set selection problem of causal la…
DeVAn: Dense Video Annotation for Video-Language Models
Tingkai Liu, Yunzhe Tao, Haogeng Liu +5
We present a novel human annotated dataset for evaluating the ability for visual-language models to generate both short and long descriptions for real-world video clips, termed DeV…
Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction
Yiren Jian, Tingkai Liu, Yunzhe Tao +3
In this paper, we introduce , a streamlined framework designed for the pre-training of visually conditioned language generation models with high computatio…