Arnab Kumar Mondal
11 papers · 2021–2025 · 6 venues · across top CS/AI venues
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Research topics
Deep Learning
(11)
Machine Learning
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Learning Types Β· Deep Learning
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Core Methods
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Artificial Intelligence
(4)
Core AI
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Computer Vision
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Learning Types Β· Machine Learning
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Optimization & Theory
(2)
Techniques
(2)
Reinforcement Learning
(1)
Analysis
(1)
Keywords
representation learning
(4)
latent space
(3)
equivariant representation
(2)
post-training quantization
(1)
reinforcement learning
(1)
vision transformer
(1)
image classification
(1)
image generation
(1)
graph laplacian
(1)
transfer learning
(1)
domain adaptation
(1)
feature extraction
(1)
embedding space
(1)
model robustness
(1)
model architecture
(1)
self-supervised learning
(1)
visual representation
(1)
class imbalance
(1)
dynamics modeling
(1)
few-shot learning
(1)
Papers
Spectral State Space Model for Rotation-Invariant Visual Representation Learning
CVPR 2025
Surprising Effectiveness of pretraining Ternary Language Model at Scale
ICLR 2025
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory
ICLR 2024
Equivariance with Learned Canonicalization Functions
ICML 2023
Equivariant Adaptation of Large Pretrained Models
NeurIPS 2023
Few-shot Cross-domain Image Generation via Inference-time Latent-code Learning
ICLR 2023
Minority Oversampling for Imbalanced Data via Class-Preserving Regularized Auto-Encoders
AISTATS 2023
Ξ±-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay
NeurIPS 2022
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
ICML 2022
Structuring Representations Using Group Invariants
NeurIPS 2022
FlexAE: flexibly learning latent priors for wasserstein auto-encoders
UAI 2021