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ADINE: An Adaptive Momentum Method for Stochastic Gradient Descent
Vishwak Srinivasan, Adepu Ravi Sankar, Vineeth N Balasubramanian
Two major momentum-based techniques that have achieved tremendous success in optimization are Polyak's heavy ball method and Nesterov's accelerated gradient. A crucial step in all…
STWalk: Learning Trajectory Representations in Temporal Graphs
Supriya Pandhre, Himangi Mittal, Manish Gupta +1
Analyzing the temporal behavior of nodes in time-varying graphs is useful for many applications such as targeted advertising, community evolution and outlier detection. In this pap…
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader +1
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. However, these deep models are perceived as "black b…
Attentive Semantic Video Generation using Captions
Tanya Marwah, Gaurav Mittal, Vineeth N. Balasubramanian
This paper proposes a network architecture to perform variable length semantic video generation using captions. We adopt a new perspective towards video generation where we allow t…
Multiresolution Match Kernels for Gesture Video Classification
Hemanth Venkateswara, Vineeth N. Balasubramanian, Prasanth Lade +1
The emergence of depth imaging technologies like the Microsoft Kinect has renewed interest in computational methods for gesture classification based on videos. For several years no…
Are Saddles Good Enough for Deep Learning?
Adepu Ravi Sankar, Vineeth N Balasubramanian
Recent years have seen a growing interest in understanding deep neural networks from an optimization perspective. It is understood now that converging to low-cost local minima is s…