Learning

Links to this note

A critique of pure learning and what artificial neural networks can learn from animal brains
A Distributional Code for Value in Dopamine-based Reinforcement Learning
Actor-Critic
All-reduce
alonso_current_2019: Current Research Trends in Robot Grasping and Bin Picking
Applying Forecasting in Finance
Artificial Intelligence
Bayesian Deep Learning
Byron Boots - Perspectives on Machine Learning and Robotics
Canonical Correlation Analysis
chen20_simpl_framew_contr_learn_visual_repres: A simple framework for contrastive learning of visual representations
chen_big_2020: Big Self-Supervised Models are Strong Semi-Supervised Learners
Co-learning
Coding Interview Preparation
Concept Grounding
Concept Learning
Contrastive Methods
Control As Inference
Data Science
Data Visualization
Datacouncil.ai Conference Notes
Deep Learning
Deep Learning Tools
Deep Learning With Bayesian Principles - Emtiyaz Khan
Deep Reinforcement Learning
Deep Reinforcement Learning That Matters
Definition of Deep Learning
Differentiable plasticity: training plastic neural networks with backpropagation
Distributed Reinforcement Learning
Dynamic Time Warping
Empirical Risk Minimization
Exploration In Reinforcement Learning
Free-Energy Reinforcement Learning
Gaussian Processes
Generalisation Error
Generalization In Reinforcement Learning
Grasp Estimation
Hindsight Experience Replay
hjelm_learning_2019: Learning deep representations by mutual information estimation and maximization
Hopfield Network
How To Be a Good Forecaster
How To Know - Celeste Kidd
How to Make Yourself Into a Learning Machine - Superorganizers
How To Take Smart Notes
Human Behaviour As Optimal Control
Imitation Learning
Inductive Bias
Information Theory
Inverse Reinforcement Learning
IS1103: Computing and Society
Learning How To Do Hard Things
Learning How To Learn
LeCun's Cake Analogy
Likelihood Principle
Lottery Ticket Hypothesis
Machine Learning
Machine Teaching
Markov Decision Process
Meta Learning
Model Compression
Model-Based Reinforcement Learning
Monte Carlo Methods
Multi-modal Alignment
Multi-modal Fusion
Multi-modal Machine Learning
Multi-modal Representation
Multi-modal Translation
Multiple Learning Kernel
Natural Language Processing
Neural Network Optimizer
Neural Ordinary Differential Equations
Neuroscience and Reinforcement Learning
Neuroscience Experimental Evidence
Options Framework
PAC Learning
Playing Atari with Deep RL
Policy Gradients
Probabilistic Graph Models
Pruning Neural Networks
Q-Learning
Quantization
Rademacher Complexity
Reinforcement Learning
Riken AIP Workshop 2019
RSS 2020, Early Career Award Keynote + Q&A: Jeannette Bohg - YouTube
RSS Feeds
Self-supervised Learning
Self-Supervised Representation Learning
Singapore Society
Single Layer XOR
SNN Software
Spiking Neural Networks
SSNLP Conference Notes
Statistical Learning
Surrogate Gradient Learning In Spiking Neural Networks
The Bias-Complexity Tradeoff
The Learning Problem
The Paths Perspective on Value Learning
Topic Modeling
Transfer Learning
Transformer Models
Uncertainty in Robotics
Unsupervised Learning
Variational Inference
VC-Dimension
VisGel
XGBoost
Zero shot Learning
zhu_ev-flownet_2018: EV-FlowNet: self-supervised optical flow estimation for event-based cameras
zhu_unsupervised_2018: Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion