Satwik Bhattamishra
11 papers · 2019–2025 · 7 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+4 more ↓ Show less ↑
π Interdisciplinary Bridge Β· Bronze πΊοΈ Taxonomy Completionist (23) π Conference Polyglot Β· Silver π£ Hot Topic Early Bird π Academic Marathon Β· Sprint
β
The Questioner
π₯
Unstoppable Β· Bronze
π
Century Club Β· Bronze
ποΈ
Keyword Collector
(60)
Top co-authors
Research topics
Machine Learning
(10)
Deep Learning
(7)
Optimization & Theory Β· Machine Learning
(7)
Architectures
(6)
Artificial Intelligence
(4)
Core AI
(4)
Core Methods
(3)
Learning Types
(3)
Optimization & Theory Β· Deep Learning
(3)
Natural Language Processing
(2)
Understanding
(2)
Mathematics & Optimization
(1)
Application Areas
(1)
Generation
(1)
Learning Paradigms
(1)
Keywords
positional encoding
(3)
transformer model
(2)
attention mechanism
(2)
recurrent neural network
(2)
neural network optimization
(2)
transformer architecture
(2)
in-context learning
(2)
natural language processing
(1)
self-attention mechanism
(1)
sequence modeling
(1)
model evaluation
(1)
semantic parsing
(1)
language modeling
(1)
communication complexity
(1)
question answering
(1)
compositional generalization
(1)
benchmark evaluation
(1)
computational complexity
(1)
paraphrase generation
(1)
intent classification
(1)
Papers
A Formal Framework for Understanding Length Generalization in Transformers
ICLR 2025
Separations in the Representational Capabilities of Transformers and Recurrent Architectures
NeurIPS 2024
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions
ICLR 2024
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations
EMNLP 2023
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions
ACL 2023
Revisiting the Compositional Generalization Abilities of Neural Sequence Models
ACL 2022
Are NLP Models really able to Solve Simple Math Word Problems?
NAACL 2021
On the Ability and Limitations of Transformers to Recognize Formal Languages
EMNLP 2020
On the Computational Power of Transformers and Its Implications in Sequence Modeling
CoNLL 2020
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages
COLING 2020
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
NAACL 2019