|
Hi! I am a first year graduate student in the Department of Computer Science at the University of Toronto . I'm a member of the Computer Science Robotics Group, am supervised by Florian Shkurti and am also closely working with Animesh Garg. My studies are supported by the Vector AI Scholarship and scholarships from the Department of Computer Science . I am engaged in the quest for Artificial General Intelligence. Although this quest has kept me fully occupied for the past three years, I also paint, and have a Bachelor of Arts degree in Fine Arts. Some of my paintings can be found here. I am fortunate to have been advised by fantastic researchers over the course of my undergraduate studies. Most recently, I worked with Shin-ichi Maeda at Preferred Networks Inc. Tokyo on transferring policies to different dynamics configurations at test-time. Previously, I worked under the guidance of Prof Yoshua Bengio and Prof Liam Paull at Mila, Montreal during the summer of 2018. My work at Mila focused on the problem of sim-to-real transfer of deep learning based planning algorithms in robotic navigation. Prior to this, I spent a wonderful winter at NUS, Singapore in Prof Brian Lim's lab. I continued collaborating remotely and successfully completed two projects on Recommendation Systems and Multi-Task Learning respectively. In IIT Kanpur, I have worked with Prof Nisheeth Srivastava on Human-Computer Interaction and with Prof Piyush Rai on Zero-Shot Learning and Program Correction. Email / Github / Google Scholar / Twitter |
|
|
|
|
If you have any questions / want to collaborate, feel free to send me an email! I am always excited to learn more by talking with people. |
|
I'm interested in deep reinforcement learning, robotics, machine learning, computer vision and explainable AI. Much of my current research is about exploration in the context of robotic policy learning. Previousy, I focused on developing efficient planning algorithms for robotic navigation/locomotion and learning transferable representations. Even before that I was involved in designing deep learning (and meta-learning) based algorithms, primarily for recommender systems and trying to make those algorithms explainable. I have explored a wide breadth of research topics around AI during my undergraduate studies, which have led to multiple primary author publications. Some of my papers are mentioned below. |
|
|
|
Samarth Sinha*, Homanga Bharadhwaj*, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti Under double blind review Explicitly maximizing diversity in ensembles through adversarial learning helps improve generalization, transfer, and uncertainty estimation |
|
Homanga Bharadhwaj, Shoichiro Yamaguchi, Shin-ichi Maeda ICRA, 2020 (Under review) Training dynamics conditioned policies on dynamics randomized environments and estimating dynamics parameters from off-policy data can help achieve zero-shot adaptation in an unseen test environment |
|
Homanga Bharadhwaj*, Zihan Wang*, Yoshua Bengio, Liam Paull ICRA, 2019 Adversarial domain adaptation can be used for training a gradient descent based planner in simulation and transferrring the learned model to a real navigation environment. |
|
Homanga Bharadhwaj, Homin Park, Brian Y. Lim RecSys, 2018 Recurrent Neural Network based Generative Adversarial Networks can learn to effectively model the latent preference trends of users in time-series recommendation. |
|
Homanga Bharadhwaj IJCNN, 2019 A Meta-Learning strategy can be used to develop a recommendation model that performs resonably good enough for a wide range of users and that can be cost-effectively updated during test time for a specific user |
|
A Hierarchical Multi-Task Learning model can leverage several auxiliary tasks like detection of container properties to overcome the limitation of insufficient visual cues for drinks, while predicting the healthiness of drinks from their images |
|
Adversarial Domain Adaptation appropriately incorporated in a Generative Zero Shot Learning model can help minimize domain shift and significantly enhance generalization on the unseen test classes |
|
|
|
Homanga Bharadhwaj, Nisheeth Srivastava WebSci, 2019 Passive website recommendations embedded in the new tab displays of browsers (that recommend based on frecency) inhibit peoples' propensity to visit diverse information sources on the internet |
|
|
|
Homanga Bharadhwaj Avinash Kumar Abheejeet Mohapatra IJCNN, 2019 An optimal features' classifier developed using evolutinary heuristics can be used for real time fault detection and identification |
|
Homanga Bharadhwaj EARS Workshop, SIGIR, 2018 Layer-wise relevance propagation can be used for explaaining the predictions of a convolutional neural network based recommendation model |
|
Homanga Bharadhwaj, Shruti Joshi XAI Workshop, IJCAI, 2018 A neighborhood style explanation scheme can be used as an auxiliary mechanism for interpreting the predictions of a Recurrent Neural Network based temporal recommendation model |
|
Homanga Bharadhwaj, ISSPIT, 2018 Layer-wise Relevance Propagation can used for explaining the predicitons of a Bi-directional Gated Recurrent Unit based speech recognition model |
|
|
|
Spontaneous Parametric Down Conversion is used to generate entangled photon pairs. SPDC can be studies through the lens of Wave Optics by making some simplifying theoretical assumptions without compromising on empirical results. Also, a simulation for SPDC can be conveniently designed, given the assumptions. |
|
|
|
|
 |
Teaching Assitant (TA), |
 |
Student Volunteer at
ACM RecSys 2018, Vancouver, BC
|
|
I love his website design. |
|
Miscellaneous stuffs - Co-authors / Fav books / My paintings / Travel / Love / |