News Updates
Teng, D & Dasgupta, S. (2019). Lifelong learning via Online Leverage Score Sampling. 1st Adaptive & Multitask Learning Workshop, ICML 2019. (rated in Top 50% selected as contributed talk).
Yu, P., Lee, J. S., Kulyatin, I., Shi, Z., & Dasgupta, S*. (2019). Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization. arXiv preprint arXiv:1901.08740. Real-world Sequential Decision Making Workshop ICML 2019 *correspondence
Joint work with IBM Research AI, ""Internal Model from Observations for Reward Shaping" will be presented at AAAI 2019 workshop on Reinforcement Learning in Games.
Paper on "Continuous Time-series Forecasting with Deep and Shallow Stochastic Processes" presented at NeuraIPS 2018 workshop on Continual Learning.
Paper on "Internal Model from Observations for Reward Shaping" presented at ICML 2018 Workshop on Adaptive Learning Agents (short talk).
Paper on "TRANSFER LEARNING FROM SYNTHETIC TO REAL IMAGES USING VARIATIONAL AUTOENCODERS FOR PRECISE POSITION DETECTION" accepted at ICIP 2018 (oral)
My paper on "Transfer learning from synthetic to real images using variational autoencoders for robotic applications" is now available on Arxiv https://arxiv.org/abs/1709.06762
Our paper on "Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions" will be presented in the Time-series workshop at NIPS 2017
Our paper on "Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA" will be presented in the MLPCD workshop at NIPS 2017
Dasgupta S. and Osogami T. "Nonlinear Dynamic Boltzmann Machines for Time-series Prediction" accepted at AAAI 2017 (acceptance rate of 24.6% >2500 submissions)
Dasgupta et al. " Regularized Dynamic Boltzmann Machine with Delay Pruning for Unsupervised Learning of Temporal Sequences", ICPR 2016 (oral presentation <14% acceptance)
Abstract selected for Talk at COSYNE 2016 main meeting. <5% acceptance (talks) (http://www.cosyne.org)
