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Xin Jiang

70 papers hereh-index 334.6k citations91 works total

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

author position
  • middle author66
  • last author4

Across the 70 of 70 papers where every author was matched, so the position is known.

fields
  • cs.CL60
  • cs.LG4
  • cs.CV3
  • eess.AS3
same name
  • Xin Jiang — 29 papers, h 26
  • Xin Jiang — 18 papers, h 13
  • Xin Jiang — 14 papers, h 4
  • Xin Jiang — 11 papers, h 6
  • Xin Jiang — 9 papers, h 5
  • Xin Jiang — 8 papers, h 6

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
20162023
most citedFILIP: Fine-grained Interactive Language-Image Pre-Training

206 citations · 624 across the 70 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022★ 24 cited

PanGu-Coder: Program Synthesis with Function-Level Language Modeling

Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta +19

We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of programming language solut…

cs.LG2021★ 8 cited

Improved OOD Generalization via Adversarial Training and Pre-training

Mingyang Yi, Lu Hou, Jiacheng Sun +4

Recently, learning a model that generalizes well on out-of-distribution (OOD) data has attracted great attention in the machine learning community. In this paper, after defining OO…

cs.LG2021★ 2 cited

Reweighting Augmented Samples by Minimizing the Maximal Expected Loss

Mingyang Yi, Lu Hou, Lifeng Shang +3

Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples e…

cs.LG2019★ 1 cited

Neural Subgraph Isomorphism Counting

Xin Liu, Haojie Pan, Mutian He +3

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Different from other traditional graph learning problems such as node classification…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.