no code implementations • 7 Oct 2021 • Jayanth Reddy Regatti, Aniket Anand Deshmukh, Frank Cheng, Young Hun Jung, Abhishek Gupta, Urun Dogan
We address this performance gap with a policy transfer algorithm which first trains a teacher agent using the offline dataset where features are fully available, and then transfers this knowledge to a student agent that only uses the resource-constrained features.
no code implementations • 29 Sep 2021 • Jayanth Reddy Regatti, Aniket Anand Deshmukh, Young Hun Jung, Frank Cheng, Abhishek Gupta, Urun Dogan
We address this performance gap with a policy transfer algorithm which first trains a teacher agent using the offline dataset where features are fully available, and then transfers this knowledge to a student agent that only uses the resource-constrained features.
1 code implementation • 4 May 2021 • Aniket Anand Deshmukh, Jayanth Reddy Regatti, Eren Manavoglu, Urun Dogan
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must be close in the representation space (exemplar consistency), and/or similar images must have similar cluster assignments (population consistency).
Ranked #3 on Image Clustering on ImageNet-10
no code implementations • 1 Jan 2021 • Levi Boyles, Aniket Anand Deshmukh, Urun Dogan, Rajesh Koduru, Charles Denis, Eren Manavoglu
Semantic hashing methods have been explored for learning transformations into binary vector spaces.
no code implementations • 3 Oct 2020 • Jayanth Reddy Regatti, Aniket Anand Deshmukh, Eren Manavoglu, Urun Dogan
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must either be closer in the representation space, or have a similar cluster assignment.
1 code implementation • 17 Aug 2020 • Udit Maniyar, Joseph K J, Aniket Anand Deshmukh, Urun Dogan, Vineeth N. Balasubramanian
Domain generalization (DG) methods try to learn a model that when trained on data from multiple domains, would generalize to a new unseen domain.
no code implementations • 18 Mar 2020 • Aniket Anand Deshmukh, Abhimanu Kumar, Levi Boyles, Denis Charles, Eren Manavoglu, Urun Dogan
In the usual self-supervision, we learn implicit labels from the training data for a secondary task.
no code implementations • 18 Feb 2020 • Abhimanu Kumar, Aniket Anand Deshmukh, Urun Dogan, Denis Charles, Eren Manavoglu
We show faster convergence rate with valid transformations for convex as well as certain family of non-convex objectives along with the proof of convergence to the original set of optima.
1 code implementation • 12 Jan 2020 • Mansi Ranjit Mane, Aniket Anand Deshmukh, Adam J. Iliff
C. elegans is commonly used in neuroscience for behaviour analysis because of it's compact nervous system with well-described connectivity.
no code implementations • ECCV 2020 • Urun Dogan, Aniket Anand Deshmukh, Marcin Machura, Christian Igel
We propose a novel curriculum learning approach for image classification that adapts the loss function by changing the label representation.
no code implementations • 23 Sep 2019 • Yuren Zhong, Aniket Anand Deshmukh, Clayton Scott
This work studies reinforcement learning in the Sim-to-Real setting, in which an agent is first trained on a number of simulators before being deployed in the real world, with the aim of decreasing the real-world sample complexity requirement.
no code implementations • 24 May 2019 • Aniket Anand Deshmukh, Yunwen Lei, Srinagesh Sharma, Urun Dogan, James W. Cutler, Clayton Scott
Domain generalization is the problem of assigning labels to an unlabeled data set, given several similar data sets for which labels have been provided.
no code implementations • 17 Oct 2018 • Aniket Anand Deshmukh, Srinagesh Sharma, James W. Cutler, Mark Moldwin, Clayton Scott
Contextual bandits are a sub-class of MABs where, at every time step, the learner has access to side information that is predictive of the best arm.
no code implementations • 9 Jul 2018 • Aniket Anand Deshmukh, Ankit Bansal, Akash Rastogi
We address the problem of domain generalization where a decision function is learned from the data of several related domains, and the goal is to apply it on an unseen domain successfully.
2 code implementations • 21 Nov 2017 • Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott
In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner.
no code implementations • NeurIPS 2017 • Aniket Anand Deshmukh, Urun Dogan, Clayton Scott
Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications.