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Jianfei Chen

58 papers hereh-index 327.9k citations76 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author41
  • last author11

Across the 58 of 58 papers where every author was matched, so the position is known.

fields
  • cs.LG40
  • stat.ML8
  • cs.CV5
  • cs.CL3
  • cs.AI1
  • cs.DC1
same name
  • Jianfei Chen — 16 papers, h 11
  • Jianfei Chen — 6 papers, h 3
  • Jianfei Chen — 1 paper
  • Jianfei Chen — 1 paper, h 1
  • Jianfei Chen — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142026
most citedDPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

290 citations · 889 across the 57 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CV2026

Visual Representation Matters: Exploiting Temporal Differences in Video-to-Audio Generation

Zehua Chen, Junyou Wang, Yuxuan Jiang +5

Video-to-audio (V2A) generation extends image-to-audio generation (I2A) by introducing consecutive frames that provide essential temporal cues for audio synthesis. However, existin…

cs.LG2026

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

Han Wang, Jintao Zhang, Kai Jiang +3

LLM-based Triton kernel generation has attracted significant interest, yet a fundamental empirical question remains unanswered: where does this capability break down, and why? We p…

cs.LG2026

SageBwd: A Trainable Low-bit Attention

Jintao Zhang, Marco Chen, Haoxu Wang +5

Low-bit attention, such as SageAttention, has emerged as an effective approach for accelerating model inference, but its applicability to training remains poorly understood. In pri…

cs.LG2026

Deterministic Differentiable Structured Pruning for Large Language Models

Weiyu Huang, Pengle Zhang, Xiaolu Zhang +3

Structured pruning reduces LLM inference cost by removing low-importance architectural components. This can be viewed as learning a multiplicative gate for each component under an…

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