Before I joined Oxford, I spent 2 amazing years as a Research Fellow at Microsoft Research India, where I worked with Dr. Navin Goyal. Prior to that, I spent a wonderful semester working with Dr. Partha Talukdar in the Indian Institute of Science. I (used to) occasionally solve problems on websites like Kaggle and answer questions on stats.stackexchange forum. I graduated with B.E. (Hons.) in Computer Science and Int. M.Sc. (Hons.) in Biological Science from BITS Pilani, India in 2019. For more details, refer to my CV or drop me an email.
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions
, Arkil Patel, Phil Blunsom, Varun Kanade
ICLR'24 [Oral]
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abstract
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions
, Arkil Patel, Varun Kanade, Phil Blunsom
ACL'23
pdf
code
abstract
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations
Arkil Patel, , Siva Reddy, Dzmitry Bahdanau
EMNLP'23 [Oral]
pdf
abstract
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
Amey Agrawal, Sameer Reddy, , Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov
Preprint'23
pdf
abstract
Revisiting the Compositional Generalization Abilities of Neural Sequence Models
Arkil Patel, , Phil Blunsom, Navin Goyal
ACL'22
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code
abstract
Are NLP Models really able to Solve Simple Math Word Problems?
Arkil Patel, , Navin Goyal
NAACL'21
pdf
code
abstract
article
On the Ability and Limitations of Transformers to Recognize Formal Languages
, Kabir Ahuja, Navin Goyal
EMNLP'20
pdf
code
abstract
On the Practical Ability of RNNs to Recognize Hierarchical Languages
Best Short Paper Award
, Kabir Ahuja, Navin Goyal
COLING'20
pdf
code
abstract
On the Computational Power of Transformers and its Implications in Sequence Modeling
, Arkil Patel, Navin Goyal
CoNLL'20
pdf
code
abstract
Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
Pratik Joshi, Christain Barnes, Sebastin Santy, Simran Khanuja,
Sanket Shah, Anirudh
Srinivasan, , Sunayana Sitaram, Monojit Choudhury, Kalika Bali
ICON'19
pdf
abstract
cite
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
Ashutosh Kumar*, , Manik Bhandari, Partha Talukdar
NAACL'19 [Oral]
pdf
code
abstract
LibNMF
An easy to use python library with implementations of a set of tested optimization and regularization methods of NMF. Implemented Algorithms include graph regularized NMF, probabilistic NMF, a first-order primal-dual algorithm ...etc
Github
PyDPP
A python package available in pip with modules for sampling from Determinantal Point Processes (DPP). Contains implementations of algorithms to sample from DPPs that encourage diversity in the selection of a subset of points from a grounded superset.
Github
- Fall 2023: Class Tutor and Marker for Computational Learning Theory
- Fall 2022: Class Tutor and Marker for Computational Learning Theory
- Spring 2018: Teaching assistant for Neural Networks and Fuzzy Logic
