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Kumar Krishna Agrawal
I'm a PhD student in AI at UC Berkeley, where I'm affiliated with BAIR and UCSF. I am incredibly fortunate to be advised by Prof. Adam Yala and Prof. Trevor Darrell. I also work closely with Sky Computing Lab at Berkeley, and Prof. Blake Richards at MILA.
My current research interests broadly lie in building data and compute-efficient algorithms and systems for human-centric AI. In particular, I'm interested on deep multimodal representation learning and its applications in healthcare and robotics.
Previously, I was an AI resident at Google Brain, where I helped build fast, simple AI algorithms. Among others, I worked on off-policy robot learning for robot manipulation and deep generative models for high-fidelity audio synthesis and program synthesis.
Before Berkeley, I studied mathematics and computer science at IIT Kharagpur, and was advised by Prof. Pabitra Mitra and Prof. Somesh Kumar. I have also been fortunate to spend time at MILA, advised by Prof. Yoshua Bengio.
about /
github /
gscholar
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Attribute diversity determines the systematicity gap in VQA
Ian Berlot-Attwell, KKA, Annabelle M. Carrell, Yash Sharma, Naomi Saphra
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Harnessing small projectors and multiple views for efficient vision pretraining
KKA, Arna Ghosh, Shagun Sodhani, Adam Oberman, Blake Richards
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Pangolin: Live video analytics without profiling
Gur-Eyal Sela, KKA, Bharath Balaji, Joseph Gonzalez, Ion Stoica
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On Different Faces of Model Scaling in Supervised and Self-Supervised Learning
Matteo Gamba, Arna Ghosh, KKA, Blake Richards, Hossein Azizpour, Mårten Björkman
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Neural population geometry across model scale: a tool for cross-species functional comparison of visual brain regions
Arna Ghosh, KKA, Zahraa Chorghay, Arnab Kumar Mondal, Blake Richards
Cosyne, 2023
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Assessing representation quality in ssl by measuring eigenspectrum decay
Arna Ghosh, KKA, Arnab Kumar Mondal, Blake Richards
Neurips, 2022
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Octopus : Low-latency & adaptive perception pipelines
Gur-Eyal Sela, Ionel Gog, Justin Wong, KKA, Sukrit Kalra, Peter
Schafhalter, Xiangxi Mo, Xin Wang, Bharath Balaji, Ion Stoica, Joseph Gonzalez
ECCV, 2022
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Learning from an exploring demonstrator: Optimal reward estimation for bandits
Wenshuo Guo, KKA, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady
AISTATS, 2022
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Discrete flows: invertible generative models for discrete data
Dustin Tran, Keyon Vafa, KKA, Laurent Dinh, Ben Poole
Neurips, 2019
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Gansynth : Adversarial Neural Audio Synthesis
Jesse Engel, KKA, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts
ICLR, 2019
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Discriminator actor critic: Addressing sample
inefficiency and reward bias in adversarial
imitation learning
Ilya Kostrikov, KKA, Debidatta Dwibedi, Sergey Levine, Jonathan Tompson
ICLR, 2019
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Towards Mixed Optimization for Reinforcement Learning with Program Synthesis
Surya Bhupatiraju, KKA, Rishabh Singh
Workshop on NAMPI, ICML, 2018
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