Home /
Research
Showing 187 - 192 / 897
Towards Neural Programming Interfaces
ZacharyBrown, NathanielRobinson, DavidWingate....
Published date-12/01/2020
LanguageModelling, TextGeneration
It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models. We recast the problem of controlling natural language generation as that of …
Rethinking Learnable Tree Filter for Generic Feature Transform
LinSong, YanweiLi, ZhengkaiJiang....
Published date-12/01/2020
InstanceSegmentation, ObjectDetection, SemanticSegmentation
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. Nevertheless, the intrinsic geometric constraint forces it to focus on the regions with close spatial …
KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization
HetShah, AvishreeKhare, NeelayShah....
Published date-11/30/2020
ModelCompression, Quantization
In recent years, the growing size of neural networks has led to a vast amount of research concerning compression techniques to mitigate the drawbacks of such large sizes. Most of …
Where to Explore Next? ExHistCNN for History-aware Autonomous 3D Exploration
YimingWang, AlessioDelBue....
Published date-11/30/2020
3DReconstruction
In this work we address the problem of autonomous 3D exploration of an unknown indoor environment using a depth camera. We cast the problem as the estimation of the Next …
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
GaurangSriramanan, SravantiAddepalli, AryaBaburaj....
Published date-11/30/2020
AdversarialAttack, AdversarialDefense
Advances in the development of adversarial attacks have been fundamental to the progress of adversarial defense research. Efficient and effective attacks are crucial for reliable evaluation of defenses, and also …
Optimizing the Neural Architecture of Reinforcement Learning Agents
N.Mazyavkina, S.Moustafa, I.Trofimov....
Published date-11/30/2020
NeuralArchitectureSearch
Reinforcement learning (RL) enjoyed significant progress over the last years. One of the most important steps forward was the wide application of neural networks. However, architectures of these neural networks …