Канал: DSAI by Dr. Osbert Tay

Deep Learning Demystified | Will Ramey Nvidia GTC

Deep Learning Demystified | Will Ramey Nvidia GTC

Applied AI | Foundation Models of Scientific Knowledge for Chemistry

Applied AI | Foundation Models of Scientific Knowledge for Chemistry

Dataset | Tutorial on Theory and Practice of Efficient and Accurate Dataset Construction | NeurIPS

Dataset | Tutorial on Theory and Practice of Efficient and Accurate Dataset Construction | NeurIPS

Lifelong Learning | Tutorial on Lifelong Learning Approaches - Part 2 | NeurIPS

Lifelong Learning | Tutorial on Lifelong Learning Approaches - Part 2 | NeurIPS

Lifelong Learning | Tutorial on Lifelong Learning Machines (Panel Talk) | NeurIPS

Lifelong Learning | Tutorial on Lifelong Learning Machines (Panel Talk) | NeurIPS

Yoshua Bengio sharing on his Deep Learning Journey

Yoshua Bengio sharing on his Deep Learning Journey

Role play with Large Language Model #LLM | Thomas Wolf

Role play with Large Language Model #LLM | Thomas Wolf

150X FASTER DataFrame Processing !!!#deeplearning #pandas #df #nvidia #rapids #science #ai

150X FASTER DataFrame Processing !!!#deeplearning #pandas #df #nvidia #rapids #science #ai

Deep Learning | A Deep Learning Journey | Yoshua Bengio | NeurIPS

Deep Learning | A Deep Learning Journey | Yoshua Bengio | NeurIPS

Applied AI | Insights from NVIDIA Research | Bill Dally

Applied AI | Insights from NVIDIA Research | Bill Dally

CV | Raytracing for Deep Neural Networks Training

CV | Raytracing for Deep Neural Networks Training

NLP | Faster Transformer

NLP | Faster Transformer

3D | Deep Learning for 3D Vision

3D | Deep Learning for 3D Vision

Data Visualization | High-Performance, Remote Scientific Visualization in Jupyter Notebooks

Data Visualization | High-Performance, Remote Scientific Visualization in Jupyter Notebooks

Tabular Data | Google TabNet: Interpretable Tabular Data Learning | AAAI

Tabular Data | Google TabNet: Interpretable Tabular Data Learning | AAAI

Meta-Learning | AAAI Tutorial

Meta-Learning | AAAI Tutorial

Deep Learning | Learning with Small Data

Deep Learning | Learning with Small Data

Applied AI | Drug Discovery

Applied AI | Drug Discovery

Deep Learning | Deep Randomized Neural Network

Deep Learning | Deep Randomized Neural Network

Time Series | Informer - Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Time Series | Informer - Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Self-Supervised Learning | Curiosity Driven Exploration by Self-Supervised Prediction

Self-Supervised Learning | Curiosity Driven Exploration by Self-Supervised Prediction

Self-Supervised Learning | Self-supervision for Learning from the Bottom Up by Alexei A. Efros

Self-Supervised Learning | Self-supervision for Learning from the Bottom Up by Alexei A. Efros

Representation Learning | Compression with Implicit Neural Representations

Representation Learning | Compression with Implicit Neural Representations

General AI | Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator

General AI | Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator

Time Series | TS Classification at Scale Geoff Webb | UoA Machine Learning Seminar

Time Series | TS Classification at Scale Geoff Webb | UoA Machine Learning Seminar

Diabetic Retinopathy Classification using Deep Learning by Mohammad T. Al-Antary

Diabetic Retinopathy Classification using Deep Learning by Mohammad T. Al-Antary

Self-supervised learning in Computer Vision with Yann LeCun, Alfredo Canziani, Ishan Misra

Self-supervised learning in Computer Vision with Yann LeCun, Alfredo Canziani, Ishan Misra

Speech recognition and Graph Transformer Networks with Yann LeCun, Alfredo Canziani, Awni Hannun

Speech recognition and Graph Transformer Networks with Yann LeCun, Alfredo Canziani, Awni Hannun

NLP | Attentions and Transformers by Alfredo Canziani

NLP | Attentions and Transformers by Alfredo Canziani

Geometry | Geometric Deep Learning by Michael Bronstein

Geometry | Geometric Deep Learning by Michael Bronstein

Interpretability | Interpretability and Analysis in Neural NLP

Interpretability | Interpretability and Analysis in Neural NLP

NLP | Why computational linguistics sits at the core of modern AI | prg.ai

NLP | Why computational linguistics sits at the core of modern AI | prg.ai

Channel Trailer

Channel Trailer

AI | Approaches to Understanding Artificial Intelligence | Yoshua Bengio

AI | Approaches to Understanding Artificial Intelligence | Yoshua Bengio

Deep Learning | Causal Inference and Stable Learning by Peng Cui

Deep Learning | Causal Inference and Stable Learning by Peng Cui

Graph Neural Network | Tutorial on Benchmarking GNN by Xavier Bresson & Yoshua Bengio

Graph Neural Network | Tutorial on Benchmarking GNN by Xavier Bresson & Yoshua Bengio

Self-Supervised Learning | Yann LeCun's Tutorial at NeurIPS

Self-Supervised Learning | Yann LeCun's Tutorial at NeurIPS

Graph Neural Network | Understanding Attention in GNN

Graph Neural Network | Understanding Attention in GNN

CV | Vision Transformer (ViT)

CV | Vision Transformer (ViT)

Signal Processing | Tutorial - Part 1

Signal Processing | Tutorial - Part 1

Bayesian Deep Learning | Tutorial - Part 1

Bayesian Deep Learning | Tutorial - Part 1

AI | How do we build AI by Tomas Mikolov #word2vec #embedding | prg.ai

AI | How do we build AI by Tomas Mikolov #word2vec #embedding | prg.ai

Deep Learning | Tutorial on Deep Learning Theory | ICML

Deep Learning | Tutorial on Deep Learning Theory | ICML

Representation Learning | Tutorial on Representation Learning Without Labels | ICML

Representation Learning | Tutorial on Representation Learning Without Labels | ICML

Representation Learning | Tutorial on Representation Learning Without Labels- Part 2 | ICML

Representation Learning | Tutorial on Representation Learning Without Labels- Part 2 | ICML

Representation Learning | Tutorial on Representation Learning Without Labels- Part 1 | ICML

Representation Learning | Tutorial on Representation Learning Without Labels- Part 1 | ICML

Deep Learning | Dissecting Neural Network by Prof. Antonio Torralba, MIT

Deep Learning | Dissecting Neural Network by Prof. Antonio Torralba, MIT

Interview with Professor Yoshua Bengio on AI, ML and Deep Learning

Interview with Professor Yoshua Bengio on AI, ML and Deep Learning

AI Gossip | AI Debate between Yoshua Bengio & Gary Marcus

AI Gossip | AI Debate between Yoshua Bengio & Gary Marcus

Deep Learning | AI Horizons Keynote by Yoshua Bengio

Deep Learning | AI Horizons Keynote by Yoshua Bengio

Applied AI | PyTorch From Research to Production | Nvidia GTC 2020

Applied AI | PyTorch From Research to Production | Nvidia GTC 2020

NLP | Text Data Augmentation with PyTorch datasets

NLP | Text Data Augmentation with PyTorch datasets

Self-Supervised Learning | The Applied Math | Yann LeCun

Self-Supervised Learning | The Applied Math | Yann LeCun

Graph Neural Network | GCNN for Web-Scale Recommender Systems | Rex Ying (Stanford) at KDD

Graph Neural Network | GCNN for Web-Scale Recommender Systems | Rex Ying (Stanford) at KDD

Applied AI | Baidu Smart City AI 百度城市大脑 | Baidu ABC SUMMIT

Applied AI | Baidu Smart City AI 百度城市大脑 | Baidu ABC SUMMIT

Interpretability | Tutorial on What is Counterfactuals in Interpretable AI? | ACL

Interpretability | Tutorial on What is Counterfactuals in Interpretable AI? | ACL

Interpretability | Tutorial on Saliency Map In Understanding Your AI Model | NeurIPS

Interpretability | Tutorial on Saliency Map In Understanding Your AI Model | NeurIPS

Interpretability | Tutorial on State of the art in Explaining Machine Learning Predictions | NeurIPS

Interpretability | Tutorial on State of the art in Explaining Machine Learning Predictions | NeurIPS

Life-long Learning | Learning to Continually Learn by Jeff Clune #OpenAI #ICML

Life-long Learning | Learning to Continually Learn by Jeff Clune #OpenAI #ICML

Elon Musk Interview At Axel Springer Award | SpaceX

Elon Musk Interview At Axel Springer Award | SpaceX

NLP | Dive into Deep Learning for NLP - Part 1

NLP | Dive into Deep Learning for NLP - Part 1

Graph Neural Network | Tutorial on Graph-based Deep Learning in NLP | Part 2

Graph Neural Network | Tutorial on Graph-based Deep Learning in NLP | Part 2

Graph Neural Network | Tutorial on Graph-based Deep Learning in NLP | Part 1 #acl #emnlp #ijcnlp #tf

Graph Neural Network | Tutorial on Graph-based Deep Learning in NLP | Part 1 #acl #emnlp #ijcnlp #tf

Audio | DeepMind's WaveNet: How it works, and how it is evolving #TensorFlow #DeepLearning

Audio | DeepMind's WaveNet: How it works, and how it is evolving #TensorFlow #DeepLearning

Graph Neural Network | Attention Guided GCN for Relation Extraction

Graph Neural Network | Attention Guided GCN for Relation Extraction

Graph Neural Network | Densely Connected GCN for Graph to Sequence Learning

Graph Neural Network | Densely Connected GCN for Graph to Sequence Learning

Graph Neural Networks and Attention model | Prof. Laura Leal-Taixé

Graph Neural Networks and Attention model | Prof. Laura Leal-Taixé

PyTorch Distributed Data Parallel | PyTorch Developer Day 2020

PyTorch Distributed Data Parallel | PyTorch Developer Day 2020

Why and How You Should Train Graph Convolutional Neural Networks | Graph+AI World 2020

Why and How You Should Train Graph Convolutional Neural Networks | Graph+AI World 2020

Open challenges in deep learning system | PyTorch Developer Day 2020

Open challenges in deep learning system | PyTorch Developer Day 2020

Programming | Python vs Rust for Simulation by Alisa Dammer

Programming | Python vs Rust for Simulation by Alisa Dammer

Deep Learning with Label Noise | Kevin McGuinness

Deep Learning with Label Noise | Kevin McGuinness

How Deepfake algorithm works | Ian Sullivan

How Deepfake algorithm works | Ian Sullivan

NYU Deep Learning Week 14 – Lecture: Structured prediction with energy-based models | Yann LeCun

NYU Deep Learning Week 14 – Lecture: Structured prediction with energy-based models | Yann LeCun

NYU Deep Learning Week 13 – Practicum: Graph Convolutional Networks (GCNs)

NYU Deep Learning Week 13 – Practicum: Graph Convolutional Networks (GCNs)

NYU Deep Learning Week 13 – Lecture: Graph Convolutional Networks (GCNs) | Xavier Bresson

NYU Deep Learning Week 13 – Lecture: Graph Convolutional Networks (GCNs) | Xavier Bresson

Weather and climate predictions with Machine Learning

Weather and climate predictions with Machine Learning

fastai v2 | Deep Learning for Coders: Lesson 1 | Jeremy Howard | Rachel Thomas

fastai v2 | Deep Learning for Coders: Lesson 1 | Jeremy Howard | Rachel Thomas

Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head CNN | MIDL 2020

Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head CNN | MIDL 2020

5 Mins Convolution and CNN Presentation + 5 Mins Bonus Material

5 Mins Convolution and CNN Presentation + 5 Mins Bonus Material

Machine learning: A New Approach to Drug Discovery | Daphne Koller | MIDL 2020

Machine learning: A New Approach to Drug Discovery | Daphne Koller | MIDL 2020

NLP | Solving #NLP Problems #BERT #Ngram #Self-Attention #Transformer

NLP | Solving #NLP Problems #BERT #Ngram #Self-Attention #Transformer

ICML2020 | Outstanding Paper | Generative Pretraining From Pixels 🔥

ICML2020 | Outstanding Paper | Generative Pretraining From Pixels 🔥

NYU Deep Learning Week 7 – Lecture: Energy based models and self supervised learning

NYU Deep Learning Week 7 – Lecture: Energy based models and self supervised learning

NYU Deep Learning Week 10 – Lecture: Self supervised learning SSL in computer vision CV

NYU Deep Learning Week 10 – Lecture: Self supervised learning SSL in computer vision CV

NYU Deep Learning Week 1 – Lecture: History, motivation, and evolution of Deep Learning

NYU Deep Learning Week 1 – Lecture: History, motivation, and evolution of Deep Learning

Workshop on Continual Learning | CVPR 2020 | Subutai Ahmad

Workshop on Continual Learning | CVPR 2020 | Subutai Ahmad

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

Knowledge Enhanced Contextual Word Representations | EMNLP-IJCNLP2019

Knowledge Enhanced Contextual Word Representations | EMNLP-IJCNLP2019

Steve Omohundro on GPT-3 | Numenta | Natural Language Processing | Transformers

Steve Omohundro on GPT-3 | Numenta | Natural Language Processing | Transformers

Towards an Understanding of Wide, Deep Neural Networks | NeurIPS 2019 | Yasaman Bahri

Towards an Understanding of Wide, Deep Neural Networks | NeurIPS 2019 | Yasaman Bahri

Tesla Andrej Karpathy in CVPR 2020: Scalability in Autonomous Driving Workshop

Tesla Andrej Karpathy in CVPR 2020: Scalability in Autonomous Driving Workshop

Causal Inference and Stable Learning by Peng Cui

Causal Inference and Stable Learning by Peng Cui

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | NAACL BEST PAPERS

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | NAACL BEST PAPERS

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning

PixelCNN, Wavenet & Variational Autoencoders - Santiago Pascual

PixelCNN, Wavenet & Variational Autoencoders - Santiago Pascual

Content-based Image Retrieval with Deep Learning - Kevin McGuinness - UPC TelecomBCN Barcelona 2019

Content-based Image Retrieval with Deep Learning - Kevin McGuinness - UPC TelecomBCN Barcelona 2019

Transfer Learning & Domain Adaptation - Ramon Morros - UPC Barcelona

Transfer Learning & Domain Adaptation - Ramon Morros - UPC Barcelona

Deep Learning for Video Analysis - Victor Campos - UPC Barcelona

Deep Learning for Video Analysis - Victor Campos - UPC Barcelona

Yann LeCun - Self Supervised Learning | ICLR 2020

Yann LeCun - Self Supervised Learning | ICLR 2020

Yoshua Bengio | A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms | ICLR 2020

Yoshua Bengio | A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms | ICLR 2020

Yoshua Bengio: Deep Learning Priors Associated With Conscious Processing | ICLR 2020

Yoshua Bengio: Deep Learning Priors Associated With Conscious Processing | ICLR 2020

ICLR 2020 | Q & A w/ Yoshua Bengio, Yann LeCun Reflections ft. Dawn Song

ICLR 2020 | Q & A w/ Yoshua Bengio, Yann LeCun Reflections ft. Dawn Song

ICLR 2020 | Yoshua Bengio, Yann LeCun Reflections (Self-Supervised Learning)

ICLR 2020 | Yoshua Bengio, Yann LeCun Reflections (Self-Supervised Learning)

Generalized Cross Entropy Loss for Noisy Labels | NeurIPS 2018

Generalized Cross Entropy Loss for Noisy Labels | NeurIPS 2018

Yoshua Bengio | Meta-transfer Learning | ICLR 2019

Yoshua Bengio | Meta-transfer Learning | ICLR 2019

DeepProbLog: Neural Probabilistic Logic Programming | NeurIPS 2018

DeepProbLog: Neural Probabilistic Logic Programming | NeurIPS 2018

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

Hyperbolic Neural Network | NeurIPS 2018 [720p]

Hyperbolic Neural Network | NeurIPS 2018 [720p]

Deep RL in a Handful of Trials using Probabilistic Dynamics Models | NeurIPS 2018

Deep RL in a Handful of Trials using Probabilistic Dynamics Models | NeurIPS 2018

Support Recovery for Orthogonal Matching Pursuit | NeurIPS 2018

Support Recovery for Orthogonal Matching Pursuit | NeurIPS 2018

Bilevel Learning of the Group Lasso Structure | NeurIPS 2018

Bilevel Learning of the Group Lasso Structure | NeurIPS 2018

Representation Learning | Fully Understanding the Hashing Trick | NeurIPS

Representation Learning | Fully Understanding the Hashing Trick | NeurIPS

Continuous Optimization for Structure Learning | NeurIPS 2018

Continuous Optimization for Structure Learning | NeurIPS 2018

GPyTorch | NeurIPS 2018

GPyTorch | NeurIPS 2018

Isolating Sources of Disentanglements in VAEs | NeurIPS 2018

Isolating Sources of Disentanglements in VAEs | NeurIPS 2018

Efficient High Dimensional Bayesian Optimization | NeurIPS 2018

Efficient High Dimensional Bayesian Optimization | NeurIPS 2018

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach | EMNLP2019

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach | EMNLP2019

Neural data-to-text generation: A comparison between pipeline and end-to-end architectures

Neural data-to-text generation: A comparison between pipeline and end-to-end architectures

Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Imagining a Future Free from the Algorithms of Oppression | Safiya Noble | ACL 2019

Imagining a Future Free from the Algorithms of Oppression | Safiya Noble | ACL 2019

EMNLP-IJCNLP2019: Text Summarization with Pretrained Encoders

EMNLP-IJCNLP2019: Text Summarization with Pretrained Encoders

EMNLP-IJCNLP2019: Mask-Predict: Parallel Decoding of Conditional Masked Language Models

EMNLP-IJCNLP2019: Mask-Predict: Parallel Decoding of Conditional Masked Language Models

Universal Adversarial Triggers for Attacking and Analyzing NLP | EMNLP IJCNLP2019

Universal Adversarial Triggers for Attacking and Analyzing NLP | EMNLP IJCNLP2019

A Topic Augmented Text Generation Model Joint Learning of Semantics and Structural Features

A Topic Augmented Text Generation Model Joint Learning of Semantics and Structural Features

Deep Learning Series: Single object detection | Jeremy Howard

Deep Learning Series: Single object detection | Jeremy Howard

NeurIPS 2019 Outstanding New Directions Paper Award w/ slides

NeurIPS 2019 Outstanding New Directions Paper Award w/ slides

Fireside Chat with Daniel Kahneman| AAAI 2019

Fireside Chat with Daniel Kahneman| AAAI 2019

Reinforcement Learning: Past, Present, and Future Perspectives (w/ slides) | NeurIPS 2019

Reinforcement Learning: Past, Present, and Future Perspectives (w/ slides) | NeurIPS 2019

How Not to Destroy the World with AI - Stuart Russell | AAAI 2020

How Not to Destroy the World with AI - Stuart Russell | AAAI 2020

Combining Machine Learning and Control for Reactive Robots - Aude Billard | AAAI-20 Invited Talk

Combining Machine Learning and Control for Reactive Robots - Aude Billard | AAAI-20 Invited Talk

AI and Security Lessons, Challenges and Future Directions | Dawn Song | AAAI Invited Talk

AI and Security Lessons, Challenges and Future Directions | Dawn Song | AAAI Invited Talk

Collaborative Machine Learning Markets | Olya Ohrimenko | NeurIPS 2019

Collaborative Machine Learning Markets | Olya Ohrimenko | NeurIPS 2019

Bridging Game Theory and Deep Learning Opening and Motivation

Bridging Game Theory and Deep Learning Opening and Motivation

Toward a General AI-Agent Architecture | Rich Sutton, DeepMind ALberta | NeurIPS 2019

Toward a General AI-Agent Architecture | Rich Sutton, DeepMind ALberta | NeurIPS 2019

Bayesian Deep Learning | NeurIPS 2019

Bayesian Deep Learning | NeurIPS 2019

Interpretable Comparison of Distributions and Models | NeurIPS 2019

Interpretable Comparison of Distributions and Models | NeurIPS 2019

Social Intelligence | Blaise Aguera y Arcas | NeurIPS 2019

Social Intelligence | Blaise Aguera y Arcas | NeurIPS 2019

Language learning with Bert | Tensorflow & Deep Learning

Language learning with Bert | Tensorflow & Deep Learning

The NEXT level Jupyter Notebook: nbdev | Tutorial by Jeremy Howard

The NEXT level Jupyter Notebook: nbdev | Tutorial by Jeremy Howard

Great Work on Object Detection: Gated Shape CNNs for Semantic Segmentation

Great Work on Object Detection: Gated Shape CNNs for Semantic Segmentation

Using spaCy with Hugging Face Transformers | Matthew Honnibal

Using spaCy with Hugging Face Transformers | Matthew Honnibal

NeurIPS 2019 | R2D2: Reliable and Repeatable Detector and Descriptor

NeurIPS 2019 | R2D2: Reliable and Repeatable Detector and Descriptor

Efficient Processing of Deep Neural Network: from Algorithms to Hardware Architectures #NeurIPS2019

Efficient Processing of Deep Neural Network: from Algorithms to Hardware Architectures #NeurIPS2019

NeurIPS 2019 | Graph Representation Learning 3

NeurIPS 2019 | Graph Representation Learning 3

NeurIPS19 Outstanding Awards| Distribution-Independent PAC Learning of Halfspaces with Massart Noise

NeurIPS19 Outstanding Awards| Distribution-Independent PAC Learning of Halfspaces with Massart Noise

Follow up Neural ODEs @ NeurIPS 2019 (NeurIPS 2018 Best Paper)

Follow up Neural ODEs @ NeurIPS 2019 (NeurIPS 2018 Best Paper)

Two Wild Yann LeCun? (Fun video at NeurIPS 2019)

Two Wild Yann LeCun? (Fun video at NeurIPS 2019)

Biological and Artificial Reinforcement Learning 1 | NeurIPS 2019

Biological and Artificial Reinforcement Learning 1 | NeurIPS 2019

Reinforcement Learning: Past, Present, and Future Perspectives | NeurIPS 2019

Reinforcement Learning: Past, Present, and Future Perspectives | NeurIPS 2019

NeurIPS 2019 | Track 3 Session 1

NeurIPS 2019 | Track 3 Session 1

NeuIPS2019 | McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bound

NeuIPS2019 | McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bound

NeurIPS 2019 | Machine Learning Meets Single Cell Biology (Invited Talk)

NeurIPS 2019 | Machine Learning Meets Single Cell Biology (Invited Talk)

NeurIPS 2019 | Track 3 Session 3

NeurIPS 2019 | Track 3 Session 3

NeurIPS 2019 | Deep Learning with Bayesian Principles by Mohammad Emtiyaz Khan

NeurIPS 2019 | Deep Learning with Bayesian Principles by Mohammad Emtiyaz Khan

NeurIPS 2019 Track 1 Session 3

NeurIPS 2019 Track 1 Session 3

NeurIPS 2019 Track 1 Session 4

NeurIPS 2019 Track 1 Session 4

NeurIPS 2019 | How to Know

NeurIPS 2019 | How to Know

NeurIPS 2019 | Multiclass Performance Metric Elicitation

NeurIPS 2019 | Multiclass Performance Metric Elicitation

NeurIPS 2019 Track 1 Session 2

NeurIPS 2019 Track 1 Session 2

NeurIPS 2019 | On Exact Computation with an Infinitely Wide Neural Net

NeurIPS 2019 | On Exact Computation with an Infinitely Wide Neural Net

NeurIPS 2019 Track 1 Session 1

NeurIPS 2019 Track 1 Session 1

NeurIPS 2019 | Disentangled Behavioural Representations

NeurIPS 2019 | Disentangled Behavioural Representations

NeurIPS 2019 Test of Time Award

NeurIPS 2019 Test of Time Award

NeurIPS 2019| Panel Discussion

NeurIPS 2019| Panel Discussion

NeurIPS 2019 | Visualizing the PHATE of Neural Networks

NeurIPS 2019 | Visualizing the PHATE of Neural Networks

NeurIPS 2019:Generalization Bounds of stochastic Gradient Descent for Wide and Deep Neural Networks

NeurIPS 2019:Generalization Bounds of stochastic Gradient Descent for Wide and Deep Neural Networks

Uniform convergence may be unable to explain generalization in deep learning | NeurIPS

Uniform convergence may be unable to explain generalization in deep learning | NeurIPS

NeurIPS2019 | Understanding the Representation Power of Graph Neural Nets in Learning Graph Topology

NeurIPS2019 | Understanding the Representation Power of Graph Neural Nets in Learning Graph Topology

Swift For TensorFlow AND FastAI: Part 4 | Jeremy Howard

Swift For TensorFlow AND FastAI: Part 4 | Jeremy Howard

Swift For TensorFlow AND FastAI: Part 3 | Jeremy Howard

Swift For TensorFlow AND FastAI: Part 3 | Jeremy Howard

Swift For TensorFlow AND FastAI: Part 2 | Jeremy Howard

Swift For TensorFlow AND FastAI: Part 2 | Jeremy Howard

Ditch Python! Swift For TensorFlow AND FastAI: Part 1 | Jeremy Howard

Ditch Python! Swift For TensorFlow AND FastAI: Part 1 | Jeremy Howard

ICML Active Learning: From Theory to Practice (Part 2)

ICML Active Learning: From Theory to Practice (Part 2)

Active Learning | Tutorial on Active Learning: From Theory to Practice - Part 1 | ICML

Active Learning | Tutorial on Active Learning: From Theory to Practice - Part 1 | ICML

Active Learning | Tutorial on Active Learning from Theory to Practice | ICML

Active Learning | Tutorial on Active Learning from Theory to Practice | ICML

ACL NLP Argument Mining for Understanding Peer Reviews

ACL NLP Argument Mining for Understanding Peer Reviews

Abusive Language Detection with Graph Convolutional Networks

Abusive Language Detection with Graph Convolutional Networks

AAAI 2019 Debate: The Future of AI

AAAI 2019 Debate: The Future of AI

Urban Computing: Building Intelligent Cities Using Big Data and AI - AAAi 2019

Urban Computing: Building Intelligent Cities Using Big Data and AI - AAAi 2019

Diversity in NLP by Mona Diab- ACL 2019

Diversity in NLP by Mona Diab- ACL 2019

Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus

Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus

Unsupervised Statistical Machine Translation

Unsupervised Statistical Machine Translation

[EMNLP 2018] TVQA: Localized, Compositional Video Question Answering

[EMNLP 2018] TVQA: Localized, Compositional Video Question Answering

The math behind Generative Adversarial Networks (GAN)

The math behind Generative Adversarial Networks (GAN)

AI in the 3D modelling industry with Blender by Andrew Price

AI in the 3D modelling industry with Blender by Andrew Price

Beginners guide to Quantum Computing by Dr Talia Gershon

Beginners guide to Quantum Computing by Dr Talia Gershon

Hyberbolic Neural Network - The new neural network framework for Geometry

Hyberbolic Neural Network - The new neural network framework for Geometry

Convolutional Neural Networks (CNN) What & How it works - Brandon Rohrer

Convolutional Neural Networks (CNN) What & How it works - Brandon Rohrer

Understanding RNN and LSTM by Brandon Rohrer

Understanding RNN and LSTM by Brandon Rohrer

Python VS C in Speed

Python VS C in Speed

You can now make iOS App & Games with Python! PyCon Australia

You can now make iOS App & Games with Python! PyCon Australia

Best Paper Awards NeurIPS 2018: Nearly Tight Sample Complexity Bounds

Best Paper Awards NeurIPS 2018: Nearly Tight Sample Complexity Bounds

Best Paper Awards NeurIPS: Optimal Algorithms for Non-Smooth Distributed Optimization in Networks

Best Paper Awards NeurIPS: Optimal Algorithms for Non-Smooth Distributed Optimization in Networks

Best Paper Awards NeurIPS 2018: Non-delusional Q-learning and Value-iteration

Best Paper Awards NeurIPS 2018: Non-delusional Q-learning and Value-iteration

ODE | Neural Ordinary Differential Equations - Best Paper Awards NeurIPS

ODE | Neural Ordinary Differential Equations - Best Paper Awards NeurIPS