Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities | AISC

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
Subscribers:
20,600
Published on ● Video Link: https://www.youtube.com/watch?v=x-DMk0F2UqY


Duration: 56:33
491 views
23

For slides and more information on the paper, visit https://aisc.ai.science/events/2019-10-08

Discussion lead: Octavian Ganea



Other Videos By LLMs Explained - Aggregate Intellect - AI.SCIENCE


2019-11-18Top-K Off-Policy Correction for a REINFORCE Recommender System | AISC
2019-11-13Overview of Unsupervised & Semi-supervised learning | AISC
2019-11-11Building products for Continous Delivery in Machine Learning | AISC
2019-11-06[WeightWatcher] Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory
2019-11-04Defending Against Fake Neural News | AISC
2019-10-28[RecSys Challenge 2019 2nd Place] Robust Contextual Models for In-Session Personalization | AISC
2019-10-23Deep learning enables rapid identification of potent DDR1 kinase inhibitors | AISC
2019-10-22Restricted Boltzmann Machines for Collaborative Filtering | AISC
2019-10-15Location Intelligence Products: Goals and Challenges | AISC
2019-10-15RecSys, Reverse Engineering User's Needs and Desires | AISC
2019-10-08Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities | AISC
2019-10-07DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker | AISC
2019-09-30AISC Abstract Night September Edition | AISC
2019-09-26EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
2019-09-26[Gated-SCNN] Gated Shape CNNs for Semantic Segmentation
2019-09-25MLOps: Overview of Machine Learning Operations on the Cloud | AISC
2019-09-24Lookahead Optimizer: k steps forward, 1 step back
2019-09-24Similarity of neural network representations revisited
2019-09-23Detecting Customer Complaint Escalation w/ Recurrent Neural Networks & Manually-Engineered Features
2019-09-23Graph Normalizing Flows
2019-09-23CNN Architectures for Large-Scale Audio Classification | AISC