Search Results for author:
Found 8 papers, 4 papers with code
Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming
We propose a subset selection problem that replaces inefficient activation layers with identity functions and optimally merges consecutive convolution operations into shallow equivalent convolution operations for efficient end-to-end inference latency.
Optimal channel selection with discrete QCQP
We first show the greedy approach of recent channel pruning methods ignores the inherent quadratic coupling between channels in the neighboring layers and cannot safely remove inactive weights during the pruning procedure.
Succinct Network Channel and Spatial Pruning via Discrete Variable QCQP
Reducing the heavy computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments.
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
Furthermore, we propose a method which avoids offloading the entire burden of jointly modeling the continuous and discrete factors to the variational encoder by employing a separate discrete inference procedure.
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization
We develop hierarchically quantized efficient embedding representations for similarity-based search and show that this representation provides not only the state of the art performance on the search accuracy but also provides several orders of speed up during inference.
EMI: Exploration with Mutual Information
Reinforcement learning algorithms struggle when the reward signal is very sparse.
EMI: Exploration with Mutual Information Maximizing State and Action Embeddings
Policy optimization struggles when the reward feedback signal is very sparse and essentially becomes a random search algorithm until the agent stumbles upon a rewarding or the goal state.
Efficient end-to-end learning for quantizable representations
To this end, we consider the problem of directly learning a quantizable embedding representation and the sparse binary hash code end-to-end which can be used to construct an efficient hash table not only providing significant search reduction in the number of data but also achieving the state of the art search accuracy outperforming previous state of the art deep metric learning methods.
