Webinars

Every week, we get to watch 30 to 45 minute webinars on various machine learning topics. These are the webinars that we watched over the last couple of years.

2023/11/08
- Upper Bound 2023: 5 Years of GPTs with Finbarr Timbers
2023/21/07
- Petar Veličković Graph Neural Networks: Geometric, Structural and Algorithmic Perspectives Part 2
2023/07/07
- Petar Veličković Graph Neural Networks: Geometric, Structural and Algorithmic Perspectives Part 1
2023/02/06
- How to write a first class paper
2023/19/05
- MedAI #78: Foundation Models for Medical AI | Vivek Natarajan
2023/27/04
- Scaling Transformer to 1M tokens and beyond with RMT (Paper Explained)
2023/02/03
- Fast Homomorphic Evaluation of Deep Discretized Neural Networks
2023/01/20
- Sharon Li, Challenges and Opportunities in Out-of-distribution Detection (Part 2)
2023/01/13
- Sharon Li, Challenges and Opportunities in Out-of-distribution Detection (Part 1)
2022/12/16
- Multimodal Machine Learning (part 2)
2022/12/9
- Multimodal Machine Learning (part 1)
2022/10/21
- βVAE representation learning for real world psychopathology
2022/10/14
- Fundamentals of Multimodal Representation Learning | Paul Pu Liang
2022/09/16
- Domain Adaptation with Invariant Representation Learning | Petar Stojanov
2022/08/19
- Datamodels - Predicting Predictions from Training Data | Andrew Ilyas
2022/08/12
- Diffusion Probabilistic Models
2022/08/05
- (Part 2) A Path Towards Autonomous AI
2022/07/15
- (Part 1) A Path Towards Autonomous AI
2022/07/04-08
- Summer school on deep learning for medical imaging
2022/05/27
- (Part 2) Equilibrium approaches to deep learning: One (implicit) layer is all you need
2022/05/13
- (Part 1) Equilibrium approaches to deep learning: One (implicit) layer is all you need
2022/04/15
- Lecture 13: Attention networks
2022/04/08
- Lecture 13: Attention networks
2022/03/25
- Transformers for Multimodal Self Supervised Learning from Raw Video, Audio and Text
2022/03/18
- ConvNeXt: Convolutions return
2022/03/11
- Negative Data Augmentation
2022/03/04
- Divide and contrast
2022/02/18
- TMLR - A New Open Journal For Machine Learning
2022/02/11
- GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
2022/02/04
- MIDL 2021, A3, Kervadec et. al., Full Paper
2022/01/28
- Interpretable Neural Networks for Computer Vision: Clinical Decisions that are Aided, not Automated
2021/12/10
- Supervised Contrastive Learning
2021/11/26
- Selected MICCAI presentations part 2
2021/11/19
- Selected MICCAI presentations part 1
2021/09/17
- Geometric Deep Learning : The Erlangen Programme of ML
2021/09/10
- Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning
2021/09/03
- Tesla AI day
2021/08/27
- Keynote A.Karpathy, Tesla
2021/07/23
- BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture search
2021/07/16
- Understanding and Visualizing Generalization UNets
2021/07/02
- AdaMatch Explained!
2021/06/18
- nnU-Net: Automated Design of Deep Learning Methods for Biomedical Image Segmentation
2021/06/04
- Towards Causal Representation Learning
2021/05/21
- Deep Learning with Differential Privacy (DP-SGD explained)
2021/05/14
- BERT Neural Network - EXPLAINED!
2021/05/07
- BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)
2021/04/30
- Lets stop talking about diversity and start working towards equity | Paloma Medina | TEDxPortland
2021/04/19-23
- Spring school on deep learning and medical imaging 2021
2021/04/16
- The Energy-Based view of Self-Supervised Learning (Y.Lecun)
2021/03/12-5
- Quantization of Neural Networks – High Accuracy at Low Precision
2021/02/26
- Nicholas Carlini: Making and Measuring Progress in Adversarial Machine Learning
2021/02/12
- [NeurIPS 2020] On the Importance of Deconstruction in Machine Learning Research
2021/02/5
- [NeurIPS 2020] Leveraging Kinematic for Deep Representation Learning NewinML
2021/01/22
- [NeurIPS 2020] Layer wise Learning of Kernel Dependence Network Workshop Beyond Backpropagation
2021/01/15
- Chundi Liu - Guided Similarity Separation for Image Retrieval
2020/12/4
- Montreal AI and Neuroscience (MAIN)
2020/11/27
- Recent Progress in Adversarial Robustness of AI Models Attacks, Defenses, and Certification
2020/11/13
- The Large Learning Rate Phase of Deep Learning with Yasaman Bahri
2020/11/6
- Deep Reinforcement Learning: CS 285 Fall 2020
2020/10/30
- MIUA 2020: Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentation
2020/10/23
- Domain Adaptation and Representation Transfer
2020/10/16
- Michael Mahoney: "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks"
2020/10/9
- Are All Features Created Equal? - Aleksander Madry
2020/9/25
- Stéphane Mallat: "Deep Generative Networks as Inverse Problems"
2020/9/18
- Representational Power of Graph Neural Networks - Stefanie Jegelka
2020/9/11
- Is Optimization the Right Language to Understand Deep Learning? - Sanjeev Arora
2020/9/4
- LSTM is dead. Long Live Transformers!
2019/10/03
- 3D deep learning (CVPR 2017)
2019/09/19
- Interview with Francois Cholet.
2019/09/12
- Capsule Networks for computer vision CVPR 2019
2019/09/05
- Introduction on deep Bayesian methods
2019/08/01
- A new perspective on adversarial-perturbations
2019/07/11-18
- ICML 2018: Tutorial Session: Toward the Theoretical Understanding of Deep Learning
2019/07/04
- Attention is all you need
2019/06/27
- Principles and applications of relational inductive biases in deep learning
2019/06/13
- Active learning
2019/05/23-30 - 06/06
- Adversarial robustness : theory and practice
2019/05/16
- Deep learning determinism
2019/05/09
- Guarding user Privacy with Federated Learning and Differential Privacy
2019/05/02
- Automatic Machine Learning
2019/05/02
- Automatic Machine Learning
2019/04/04
- Introduction to the new Beluga GPU server
2019/03/28
- Integrating Domain-Knowledge into deep learning
2019/03/21
- Variational inference, lower bound and VAE
2019/03/14
- Bayesian NN from Andrew Gordon Wilson
2019/02/28
- Bayesian NN from Andrew Gordon Wilson
2019/02/21
- Bayesian NN from Andrew Gordon Wilson
2019/02/07
- Designing Computer Systems for Software 2.0
2019/01/31
- Reproducible, Reusable, and Robust Reinforcement Learning
2019/01/24
- SecML18: Been Kim on Interpretability for when NOT to use machine learning
2018/01/17
- UMAP Uniform Manifold Approximation and Projection for Dimension Reduction | SciPy 2018 |
2018/12/20
- Making algorithm trustworthy
2018/12/06-13
- The trouble with bias by Kate Crawford
2018/11/29
- SceneGraph for Recognition and Generation - CVPR 2018 - J.Johnson (24:50)
2018/11/22
- Coco Update + Panoptic segmentation - CVPR 2018 - P.Dollar (33:23)
2018/11/14
- StackGAN++, Realistic Image Synthesis with Stacked Generative Adversarial Networks
2018/11/07
- Best Paper CVPR2018 - Taxonomy - Disentangling Task Transfer Learning
2018/10/31
- Connecting GANs, Actor Critic Methods and Multilevel Optimization, DALI 2017
2018/10/17
- ADMM by Christian Desrosiers
2018/10/10
- Software Engineering for Software 2.0
- Fast and accurate white matter bundle segmentation
2018/09/26-03
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
- transformation Autoregressive networks
2018/09/12-19
- Vitalab_ai librairy design!
2018/08/08
- ICML2018 : Bayesian inference tutorial (part 2)
2018/08/01
- ICML2018 : Bayesian inference tutorial (part 1)
2018/07/25
- ICML2018 : Adafactor and Kronecker Recurrent Units seem interesting + ICML2018 : Efficient Neural Architecture Search via Parameter Sharing (1:25:00)
2018/07/18
- Graph cut
2018/07/11
- CVPR2018 - MapNet: An Allocentric Spatial Memory for Mapping Environments, JoÃ£o F. Henriques
2018/07/04
- CVPR2018 - High-Resolution Image Synthesis and Semantic Manipulation With Conditional GANs (20:00)
2018/06/27
- CVPR2018 - Taxonomy - Disentangling Task Transfer Learning
- CVPR 2018: What do Deep Networks Like to See?
2018/06/06-2018/06/13
- A Neural Network Model That Can Reason
2018/05/30
- From Generative Models to Generative Agents
2018/05/23
- Multi-Scale Dense Networks for Resource Efficient Image Classification
2018/05/07-2018/05/14
- K.Grauman - ICLR keynote - Visual Learning with Unlabeled Video and Look Around Policies
- Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions
2018/05/02
- Train longer, generalize better: closing the generalization gap in large batch training of neural networks
- Unsupervised learning of object frames by dense equivariant image labelling
2018/04/25
- From bayesian Sparsity to Gated Recurrent Nets. + D
2018/04/18
- Spectral Clustering.
2018/02/14 - 2018/04/11
- Bayesian methods for Machine learning
2018/02/07
- Differentiable Programs with Neural Libraries
- Sharp Minima
2018/01/31
- The loss surface of deep and wide NN
- Seq2Seq Modeling
2018/01/24
- Variational Dropout Specifies Deep NN
- ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
2018/01/17
- Counterfactual Fairness
2018/01/11
- Yee Whye keynote: On Bayesian Deep Learning and Deep Bayesian Learning, at NIPS 2017
2017/12/30
- Jacob Walker: Data-Driven Visual Forecasting
2017/12/13
- Semantic Video CNNs through Representation Warping
- Detail-revealing Deep Video Super-resolution
2017/11/01
- Arvind Neelakantan: Knowledge Representation And Reasoning With Deep Neural Networks
2017/10/18
- ADMM
2017/10/11
- RNN Symposium: Nando de Freitas - Learning to Learn, to Program, to Explore and to Seek Knowledge
2017/10/04
- Differentiable Neural Computer (Alex Graves)
2017/09/27
- Generative Modelling as Sequence Learning
- Skin Cancer Image Classification
2017/09/20
- Combining online and offline knowledge in UCT
2017/08/30-6
- Deep reinforcement learning
2017/08/16-23
- Reinforcement Learning by J.Pineau
2017/07/12
- Zero-shot learning
2017/07/05
- Predictive Learning
2017/06/28
- Amortised MAP inference Image Super-resolution
- Towards Principled Methods for Training GAN
2017/06/21
- Deep learning in medical imaging
2017/06/14
- Detecting Events and Key Actors in Multi-Person Videos
- Recurrent Face Aging.
2017/06/07
- Dynamic Image Networks for Action Recognition
- 3D Action Recognition From Novel Viewpoints
2017/05/31
- Generalization and Equilibrium in Generative Adversarial Nets (GANs)
2017/05/24
- Learning Graphical State Transitions
- Neural Architecture Search with Reinforcement Learning
2017/05/17
- Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
2017/05/10
- End-to-end Optimized Image Compression
- Making neural programming generalize via recursions
2017/05/03
- Toward Efficient Deep Neural Network Deployment: Deep Compression and EIE, Song Han
2017/04/26
- What can deep learning learn from linear regression? (ICLR 2017, from 30:30 to 1:10:00)
2017/04/19
- Learning What and Where to Draw
- Without-Replacement Sampling for Stochastic Gradient Methods
2017/04/12
- MIT 6.S191 Lecture 6: Deep Reinforcement Learning
2017/04/05
- Visualizing Data Using t-SNE
2017/03/29
- Integrating Keras & TensorFlow: The Keras Workflow, Expanded
2017/03/15
- Deep learning with stochastic depth
2017/03/01
- NIPS 2016 Workshop on Adversarial Training - Ian Goodfellow - Introduction to GANs
2017/01/25
- Reasoning, Attention and Memory
- Sumit Chopra
2017/01/11
- Learning to learn and compositionality with deep recurrent neural networks
2016/12/14
- Fast Algorithms for Convolutional Neural Networks [CVPR2016]
2016/11/30
- Learning feed-forward one-shot learners [NIPS 2016]
2016/11/23
- Deep Visual Analogy-Making
2016/11/02-09
- Multi-Scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation
- Deep RNN by Y. Bengio
2016/10/26
- Multi-Scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation
- synthetic data for augmenting training data
- WIDER FACE: A Face Detection Benchmark
2016/10/19
- End-To-End People Detection in Crowded Scenes
- G-CNN: An Iterative Grid Based Object Detector
- Accurate Image Super-Resolution Using Very Deep Convolutional Networks
2016/10/12
- Instance-Aware Semantic Segmentation via Multi-Task Network Cascades
- ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
2016/10/05
- VQA Divide and Conquer
2016/09/28
- Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-Resolution Model
- Structured Prediction of Unobserved Voxels From a Single Depth Image
- DeepStereo: Learning to Predict New Views From the Worldâs Imagery
2016/09/21
- NetVLAD: CNN Architecture for Weakly Supervised Place Recognition
- Structural-RNN: Deep Learning on Spatio-Temporal Graphs
2016/09/14
- VideoLSTM: Convolves, attends, and flows for action recognitio
- LocNet: Improving Localization Accuracy for Object Detection
2016/09/07
- Deep Learning Lecture 14: Variational Autoencoders
2016/07/06
- Adaptive Deep Learning
- Spatial Transformer Networks
2016/05/25
- Semantic Segmentation: Deep Parsing Network
- Decision Forests
2016/05/18
- Minimum Barrier Salient Object Detection at 80 FPS
- Fast R-CNN
2016/05/11
- Weakly Supervised Multiclass Video Segmentation (CVPR2014)
- Large-scale Video Classification with Convolutional Neural Networks
2016/04/27
- Batch Normalization
- Seeing People with Deep Learning
2016/04/13
- High Confidence Predictions for Unrecognizable Images
- Determining Parent-Offspring Resemblance via Gated-Autoencoders
2016/04/06
- Do Statistical Models Understand the World?
- Deep Generative Models Using A Pyramid Of Adversarial Networks
2016/03/30
- Hypercolumns for Object Segmentation and Fine-Grained Localization
- Improving Object Detection With Bayesian Optimization and Structured Prediction
2016/03/09
- Undirected Graphical Models
2016/02/24 - 2016/03/02
- Deep Learning: Theoretical Motivations
2016/02/17
- Fully Convolutional Networks for Semantic Segmentation
2016/02/10
- Training Deep Convolutional Neural Networks to Play Go
2016/02/03
- Deep Edge-Aware Filters
2016/01/27
- Geodesic Object Proposals
2016/01/20
- Visualizing Data Using t-SNE
2015/11/25
- Unsupervised Object Discovery With Bottom-Up Region Proposals
- Geo-semantic Segmentation
2015/11/05
- Introduction to Markov Chains
2015/10/28
- PANDA: Pose Aligned Networks for Deep Attribute Modeling
2015/10/28
- Learning Mid-Level Image Representations using CNNs
2015/10/20
- Deeply Learned Attributes for Crowded Scene Understanding
2015/10/20
- Relative Attributes
2015/10/13
- Understanding Deep Image Representations by Inverting Them
2015/09/01
- Automated Image Captioning with ConvNets and Recurrent Nets
2015/08/25
- Statistics 101: ANOVA, A Visual Introduction part 3
2015/08/18
- Statistics 101: ANOVA, A Visual Introduction part 2
2015/08/11
- Statistics 101: ANOVA, A Visual Introduction part 1
2015/08/04
- Taking Humans out of the Deep Learning Loop
2015/07/28
- Context-Aware Saliency Detection
2015/07/14
- Whats wrong with deep learning?
2015/06/02
- How transferable are features in deep neural networks?
2015/05/26
- The Unreasonable Effectiveness Of Deep Learning
2015/05/21
- Towards On-the-fly Large Scale Video Search
2015/05/13
- High-dimensional learning with deep network contractions
2015/05/06
- Myths of Representation Learning
2015/04/22
- Segmentation Propagation in ImageNet
2015/04/15
- Hadoop, spark et big data 2.0
2015/04/08
- Learned vs Hand Feature Representations for 3D Agglomeration
2015/04/01
- Minsu Cho, Karteek Alahari, ENS-Willow and Jean Ponce
2015/03/18
- Designing effective scientific presentations: Using PowerPoint
2015/03/11
- How to Write a Great Research Paper
2015/02/25
- Backpropagation and Inference in Deep Generative Models
2015/02/18
- Hough Forest Introduction
2015/02/18
- On Detection of Multiple Object Instances using Hough Transforms
2015/02/11
- Optimization with stochastic gradient and quasi-Newton methods
2015/02/04
- Statistical Computing on Manifolds for Computational Anatomy
2015/01/28
- Machine Learning Problems Computer Vision Would Like to See Solved
2015/01/21
- Markov Logic: A Unifying Language for Knowledge Management part 2
2015/01/14
- Markov Logic: A Unifying Language for Knowledge Management part 1
2014/12/10
- Encoding vs Training with Sparse Coding and Vector Quantization
2014/12/10
- Online Dictionary Learning for Sparse Coding
2014/12/03
- Recurrent Convolutional Neural Networks for Scene Labeling
2014/11/26
- HOGgles: Visualizing Object Detection Features and Visualizing
2014/11/26
- Understanding Convolutional Networks
2014/11/19
- Dark Knowledge
2014/11/12
- Domain Adaptation presentation by Sébastien Piérard
2014/11/05
- Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
2014/10/29
- Random Forests
2014/10/22
- BING: Binarized Normed Gradients for Objectness Estimation at 300fps
2014/10/22
- Patch to the Future: Unsupervised Visual Prediction
2014/10/15
- Structured Generative Models of Natural Source Code
2014/10/08
- Rich Feature Hierarchies for Object Detection and Segmentation
2014/10/08
- Large-scale Video Classification with Convolutional Neural Networks
2014/10/01
- Machine Learning Approach to Computer Perception of Virtualization
2014/09/17
- Morphology for Word Representations and Language Modelling
2014/09/03
- Scaling with the Parameters Server Variations on a Theme