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Manually authoring transition animations for a complete locomotion system can be a tedious and time-consuming task, especially for …
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model …
The Probably Approximately Correct (PAC) Bayes framework (McAllester, 1999) can incorporate knowledge about the learning algorithm and …
Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process …
Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most …
Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric …
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational …
Recent progress in deep generative models has been fueled by two paradigms – au- toregressive and adversarial models. We propose a …
We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also …
Object counting is an important task in computer vision due to its growing demand in applications such as surveillance, traffic …