Канал: Steven Van Vaerenbergh

ChatGPT 4 system prompt (December 16, 2023)

ChatGPT 4 system prompt (December 16, 2023)

ChatGPT 3.5 system prompt (December 16, 2023)

ChatGPT 3.5 system prompt (December 16, 2023)

GitHub Copilot demonstration October 2023 (part 2)

GitHub Copilot demonstration October 2023 (part 2)

GitHub Copilot demonstration October 2023 (part 1)

GitHub Copilot demonstration October 2023 (part 1)

Demo of ChatGPT's visual capabilities (Oct. 2023)

Demo of ChatGPT's visual capabilities (Oct. 2023)

Demo of ChatGPT's Advanced Data Analysis (Oct. 2023)

Demo of ChatGPT's Advanced Data Analysis (Oct. 2023)

Geometric reasoning with ChatGPT and GeoGebra, part 2

Geometric reasoning with ChatGPT and GeoGebra, part 2

ChatGPT 3 minute presentation

ChatGPT 3 minute presentation

Copilot demonstration April 2023

Copilot demonstration April 2023

Using ChatPDF to chat with an astrophysics paper

Using ChatPDF to chat with an astrophysics paper

Officer Davis' baking channel: Strawberry cake

Officer Davis' baking channel: Strawberry cake

Using D-ID to create a talking avatar video

Using ChatPDF to automatically generate Python code from pseudocode in an academic article

Using ChatPDF to automatically generate Python code from pseudocode in an academic article

Geometric reasoning with ChatGPT and GeoGebra, part 1

Geometric reasoning with ChatGPT and GeoGebra, part 1

RevEye Reverse Image Search

RevEye Reverse Image Search

Aki Vehtari: Stan and probabilistic programming (MLSP 2020 tutorial)

Aki Vehtari: Stan and probabilistic programming (MLSP 2020 tutorial)

Razvan Pascanu: Improving learning efficiency for deep neural networks (MLSP 2020 keynote)

Razvan Pascanu: Improving learning efficiency for deep neural networks (MLSP 2020 keynote)

Ole Winther: Latent variable models from independent components to VAEs and flows (MLSP2020 keynote)

Ole Winther: Latent variable models from independent components to VAEs and flows (MLSP2020 keynote)

Michael Unser: Splines and Machine Learning: From classical RKHS methods to DNN (MLSP 2020 keynote)

Michael Unser: Splines and Machine Learning: From classical RKHS methods to DNN (MLSP 2020 keynote)

Truyen Tran - Learning to Remember More with Less Memorization (ICLR 2019 talk)

Truyen Tran - Learning to Remember More with Less Memorization (ICLR 2019 talk)

Robert Geirhos: ImageNet-trained CNNs are biased towards texture (ICLR 2019 talk)

Robert Geirhos: ImageNet-trained CNNs are biased towards texture (ICLR 2019 talk)

Steven Van Vaerenbergh Live Stream

Steven Van Vaerenbergh Live Stream

John M. Abowd: The U.S. Census Bureau Tries to be a Good Data Steward in the 21st Century

John M. Abowd: The U.S. Census Bureau Tries to be a Good Data Steward in the 21st Century

ICLR Debate with Leslie Kaelbling (ICLR 2019)

ICLR Debate with Leslie Kaelbling (ICLR 2019)

Yikang Shen: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks (ICLR2019)

Yikang Shen: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks (ICLR2019)

Pierre-Yves Oudeyer: Developmental Autonomous Learning (ICLR 2019 invited talk)

Pierre-Yves Oudeyer: Developmental Autonomous Learning (ICLR 2019 invited talk)

Ian Goodfellow: Adversarial Machine Learning (ICLR 2019 invited talk)

Ian Goodfellow: Adversarial Machine Learning (ICLR 2019 invited talk)

J. Frankle & M. Carbin: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

J. Frankle & M. Carbin: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Susan Athey: Counterfactual Inference (NeurIPS 2018 Tutorial)

Susan Athey: Counterfactual Inference (NeurIPS 2018 Tutorial)

Alex Graves and Marc'Aurelio Ranzato: Unsupervised Deep Learning (NeurIPS 2018 Tutorial)

Alex Graves and Marc'Aurelio Ranzato: Unsupervised Deep Learning (NeurIPS 2018 Tutorial)

Frank Hutter and Joaquin Vanschoren: Automatic Machine Learning (NeurIPS 2018 Tutorial)

Frank Hutter and Joaquin Vanschoren: Automatic Machine Learning (NeurIPS 2018 Tutorial)

Shawe-Taylor and Rivasplata: Statistical Learning Theory - a Hitchhiker's Guide (NeurIPS 2018)

Shawe-Taylor and Rivasplata: Statistical Learning Theory - a Hitchhiker's Guide (NeurIPS 2018)

Common Pitfalls for Studying the Human Side of Machine Learning (NeurIPS 2018 Tutorial)

Common Pitfalls for Studying the Human Side of Machine Learning (NeurIPS 2018 Tutorial)

David Dunson: Scalable Bayesian Inference (NeurIPS 2018 Tutorial)

David Dunson: Scalable Bayesian Inference (NeurIPS 2018 Tutorial)

J. Z. Kolter and A. Madry: Adversarial Robustness - Theory and Practice (NeurIPS 2018 Tutorial)

J. Z. Kolter and A. Madry: Adversarial Robustness - Theory and Practice (NeurIPS 2018 Tutorial)

Fernanda Viégas and Martin Wattenberg: Visualization for Machine Learning (NeurIPS 2018 Tutorial)

Fernanda Viégas and Martin Wattenberg: Visualization for Machine Learning (NeurIPS 2018 Tutorial)

Evolution of Apache Spark (September 12, 2018)

Evolution of Apache Spark (September 12, 2018)

Evolution of Apache Hadoop (September 11, 2018)

Evolution of Apache Hadoop (September 11, 2018)

Sanjeev Arora: Toward Theoretical Understanding of Deep Learning (ICML 2018 tutorial)

Sanjeev Arora: Toward Theoretical Understanding of Deep Learning (ICML 2018 tutorial)

Dawn Song: AI and Security: Lessons, Challenges and Future Directions (ICML 2018 invited talk)

Dawn Song: AI and Security: Lessons, Challenges and Future Directions (ICML 2018 invited talk)

Josh Tenenbaum: Building Machines that Learn and Think Like People (ICML 2018 invited talk)

Josh Tenenbaum: Building Machines that Learn and Think Like People (ICML 2018 invited talk)

Benjamin Recht: Optimization Perspectives on Learning to Control (ICML 2018 tutorial)

Benjamin Recht: Optimization Perspectives on Learning to Control (ICML 2018 tutorial)

Understanding your Neighbors: Practical Perspectives From Modern Analysis (ICML 2018 tutorial)

Understanding your Neighbors: Practical Perspectives From Modern Analysis (ICML 2018 tutorial)

Yisong Yue and Hoang M Le: Tutorial on Imitation Learning (ICML 2018 tutorial)

Yisong Yue and Hoang M Le: Tutorial on Imitation Learning (ICML 2018 tutorial)

Tamara Broderick: Variational Bayes and Beyond: Bayesian Inference for Big Data (ICML 2018 tutorial)

Tamara Broderick: Variational Bayes and Beyond: Bayesian Inference for Big Data (ICML 2018 tutorial)

Machine Learning in Automated Mechanism Design for Pricing and Auctions (ICML 2018 tutorial)

Machine Learning in Automated Mechanism Design for Pricing and Auctions (ICML 2018 tutorial)

Max Welling: Intelligence per Kilowatthour (ICML 2018 invited talk)

Max Welling: Intelligence per Kilowatthour (ICML 2018 invited talk)

Evolution of OpenAI Gym (May 15th, 2018)

Evolution of OpenAI Gym (May 15th, 2018)

Evolution of CleverHans (May 15th, 2018)

Evolution of CleverHans (May 15th, 2018)

Kristen Grauman: Visual Learning With Unlabeled Video and Look-Around Policies ICLR2018 invited talk

Kristen Grauman: Visual Learning With Unlabeled Video and Look-Around Policies ICLR2018 invited talk

Fireside Chat with Daphne Koller (ICLR 2018)

Fireside Chat with Daphne Koller (ICLR 2018)

Christopher D Manning: A Neural Network Model That Can Reason (ICLR 2018 invited talk)

Christopher D Manning: A Neural Network Model That Can Reason (ICLR 2018 invited talk)

Joelle Pineau: Reproducibility, Reusability, and Robustness in Deep Reinforcement Learning ICLR 2018

Joelle Pineau: Reproducibility, Reusability, and Robustness in Deep Reinforcement Learning ICLR 2018

Blake Richards: Deep Learning with Ensembles of Neocortical Microcircuits (ICLR 2018 invited talks)

Blake Richards: Deep Learning with Ensembles of Neocortical Microcircuits (ICLR 2018 invited talks)

Koray Kavukcuoglu: From Generative Models to Generative Agents (ICLR 2018 invited talk)

Koray Kavukcuoglu: From Generative Models to Generative Agents (ICLR 2018 invited talk)

Suchi Saria: Augmenting Clinical Intelligence with Machine Intelligence (ICLR 2018 invited talk)

Suchi Saria: Augmenting Clinical Intelligence with Machine Intelligence (ICLR 2018 invited talk)

Bernhard Schölkopf: Learning Causal Mechanisms (ICLR invited talk)

Bernhard Schölkopf: Learning Causal Mechanisms (ICLR invited talk)

Erik Brynjolfsson: What Can Machine Learning Do? Workforce Implications (ICLR 2018)

Erik Brynjolfsson: What Can Machine Learning Do? Workforce Implications (ICLR 2018)

Alex Acero: The Deep Learning Revolution (ICASSP 2018 plenary)

Alex Acero: The Deep Learning Revolution (ICASSP 2018 plenary)

Luc Vincent: Transportation as a Service using a hybrid network of drivers and self-driving vehicles

Luc Vincent: Transportation as a Service using a hybrid network of drivers and self-driving vehicles

Julia Hirschberg: "Detecting Deceptive Speech: Humans vs. Machines" (ICASSP 2018 plenary)

Julia Hirschberg: "Detecting Deceptive Speech: Humans vs. Machines" (ICASSP 2018 plenary)

Yann LeCun: Power and Limits of Deep Learning for Signal Understanding (ICASSP 2018 plenary)

Yann LeCun: Power and Limits of Deep Learning for Signal Understanding (ICASSP 2018 plenary)

Ali H. Sayed: State of the Society address at ICASSP 2018

Ali H. Sayed: State of the Society address at ICASSP 2018

Theory and Algorithms for Forecasting Non-Stationary Time Series (NIPS 2016 tutorial)

Theory and Algorithms for Forecasting Non-Stationary Time Series (NIPS 2016 tutorial)

Ian Goodfellow: Generative Adversarial Networks (NIPS 2016 tutorial)

Ian Goodfellow: Generative Adversarial Networks (NIPS 2016 tutorial)

Variational Inference: Foundations and Modern Methods (NIPS 2016 tutorial)

Variational Inference: Foundations and Modern Methods (NIPS 2016 tutorial)

Evolution of TensorFlow (Jan. 12th, 2018)

Evolution of TensorFlow (Jan. 12th, 2018)

Eight years of scikit-learn development (Jan. 11th 2018)

Eight years of scikit-learn development (Jan. 11th 2018)

Neil Lawrence: Deep Probabilistic Modelling with Gaussian Processes (NIPS 2017 tutorial)

Neil Lawrence: Deep Probabilistic Modelling with Gaussian Processes (NIPS 2017 tutorial)

Brendan Frey: Why AI Will Make it Possible to Reprogram the Human Genome (NIPS 2017 keynote)

Brendan Frey: Why AI Will Make it Possible to Reprogram the Human Genome (NIPS 2017 keynote)

Pieter Abbeel: Deep Learning for Robotics (NIPS 2017 Keynote)

Pieter Abbeel: Deep Learning for Robotics (NIPS 2017 Keynote)

Kate Crawford: The Trouble with Bias (NIPS 2017 keynote)

Kate Crawford: The Trouble with Bias (NIPS 2017 keynote)

John Platt: Powering the next 100 years (NIPS 2017 Keynote)

John Platt: Powering the next 100 years (NIPS 2017 Keynote)

Deep Learning session at NIPS 2017

Deep Learning session at NIPS 2017

Probabilistic Methods, Applications sessions at NIPS 2017

Probabilistic Methods, Applications sessions at NIPS 2017

Deep Learning: Practice and Trends (NIPS 2017 Tutorial, parts I & II)

Deep Learning: Practice and Trends (NIPS 2017 Tutorial, parts I & II)

Yee Whye Teh: On Bayesian Deep Learning and Deep Bayesian Learning (NIPS 2017 Keynote)

Yee Whye Teh: On Bayesian Deep Learning and Deep Bayesian Learning (NIPS 2017 Keynote)

Ali Rahimi - NIPS 2017 Test-of-Time Award presentation

Ali Rahimi - NIPS 2017 Test-of-Time Award presentation

Visualization of a 3D neural network (6)

Visualization of a 3D neural network (6)

Visualization of a 3D neural network (5)

Visualization of a 3D neural network (5)

Visualization of a 3D neural network (4)

Visualization of a 3D neural network (4)

Visualization of a 3D neural network (3)

Visualization of a 3D neural network (3)

Visualization of a 3D neural network (2)

Visualization of a 3D neural network (2)

Visualization of a 3D neural network (1)

Visualization of a 3D neural network (1)

OpenCV Face Detection: Visualized

OpenCV Face Detection: Visualized