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In video compression, coding efficiency is improved by reusing pixels from previously decoded frames via motion and residual compensation. We define two levels of hierarchical redundancy in video frames: 1) first-order: redundancy in pixel space, i.e., similarities in pixel values across neighboring frames, which is effectively captured using motion and residual compensation, 2) second-order: redundancy in motion and residual maps due to smooth motion in natural videos. While most of the existing neural video coding literature addresses first-order redundancy, we tackle the problem of capturing second-order redundancy in neural video codecs via predictors. We introduce generic motion and residual predictors that learn to extrapolate from previously decoded data. These predictors are lightweight, and can be employed with most neural video codecs in order to improve their rate-distortion performance. Moreover, while RGB is the dominant colorspace in neural video coding literature, we introduce general modifications for neural video codecs to embrace the YUV420 colorspace and report YUV420 results. Our experiments show that using our predictors with a well-known neural video codec leads to 38% and 34% bitrate savings in RGB and YUV420 colorspaces measured on the UVG dataset., WACV 2023

Optimizing the execution time of tensor program, e.g., a convolution, involves finding its optimal configuration. Searching the configuration space exhaustively is typically infeasible in practice. In line with recent research using TVM, we propose to learn a surrogate model to overcome this issue. The model is trained on an acyclic graph called an abstract syntax tree, and utilizes a graph convolutional network to exploit structure in the graph. We claim that a learnable graph-based data processing is a strong competitor to heuristic-based feature extraction. We present a new dataset of graphs corresponding to configurations and their execution time for various tensor programs. We provide baselines for a runtime prediction task., All authors contributed equally. Accepted as a workshop paper at Representation Learning on Graphs and Manifolds @ ICLR 2019. Fixed values in Table 1

Autonomous vehicles have to navigate the surrounding environment with partial observability of other objects sharing the road. Sources of uncertainty in autonomous vehicle measurements include sensor fusion errors, limited sensor range due to weather or object detection latency, occlusion, and hidden parameters such as other human driver intentions. Behavior planning must consider all sources of uncertainty in deciding future vehicle maneuvers. This paper presents a scalable framework for risk-averse behavior planning under uncertainty by incorporating QMDP, unscented transform, and Monte Carlo tree search (MCTS). It is shown that upper confidence bound (UCB) for expanding the tree results in noisy Q-value estimates by the MCTS and a degraded performance of QMDP. A modification to action selection procedure in MCTS is proposed to achieve robust performance., 7 pages, accepted to NeurIPS 2018 Workshop on Machine Learning for Intelligent Transportation Systems (MLITS)

In this paper, we introduce plan-time sufficient statistics, rep- resenting probability distributions over joint sets of private information, for zero-sum games of incomplete information. We define a family of zero-sum Bayesian Games (zs-BGs), of which the members share all elements but the plan-time statistic. Using the fact that the statistic can be decomposed into a marginal and a conditional term, we prove that the value function of the family of zs-BGs exhibits concavity in marginal-space of the maximizing agent and convexity in marginal-space of the minimizing agent. We extend this re- sult to sequential settings with a dynamic state, i.e., zero-sum Partially Observable Stochastic Games (zs-POSGs), in which the statistic is a probability distribution over joint action- observation histories. First, we show that the final stage of a zs-POSG corresponds to a family of zs-BGs. Then, we show by induction that the convexity and concavity properties can be extended to every time-step of the zs-POSG.