30:31
Jingfeng Zhang (RIKEN-AIP) - Adversarial robustness: from basic science to applications
01:07:36
Prof. Surya Ganguli - From statistical physics theory to machine learning algorithms: how to beat...
22:43
Berfin Simsek (EPFL-CIS) - Neural Network Loss Landscapes: Symmetry-Induced Saddles and the ...
29:48
Guillermo Ortiz-Jimenez (EPFL-CIS) - Catastrophic overfitting is a bug but also a feature
26:32
Hadrien Hendrikx (EPFL-CIS) -Beyond spectral gap: the role of the topology in decentralized learning
18:11
Hugo Cui (EPFL-CIS) - Error rates for kernel methods under source and capacity conditions
26:23
Etienne Boursier (EPFL-CIS) Gradient flow dynamics of shallow ReLU networks for square loss and...
30:37
Sebastian Neumayer (EPFL-CIS) -Lipschitz Function Approximation using DeepSpline Neural Networks
27:34
Luca Viano (EPFL-CIS) Proximal Point Imitation Learning
13:37
Auke Ijspeert - Research in The Biorobotics Laboratory (BioRob) EPFL
13:49
Prof. Devis Tuia - Environmental Computational Science and Earth Observation Laboratory
17:34
Prof. Alcherio Martinoli - Distributed Intelligent Systems and Algorithms Laboratory
15:18
Prof. Olga Fink - Intelligent Maintenance and Operations Systems
47:59
âCan artificial intelligence and machine learning help us to light up an earthbound star?â
55:27
"Building Robust Ensembles via Margin Boosting" Prof. Pradeep Ravikumar, Carnegie Mellon University
43:15
Exploring the sequence landscape space of proteins - Prof. Paolo De Los Rios
41:08
âAccelerating Chemical Synthesis with Transformersâ Prof. Philippe Schwaller, EPFL
48:58
Prof. Olga fink -âDomain adaptation and hybrid algorithms fusing physics-based and deep learning...
01:00:51
"Numerical Encoding for DNN Accelerators" Prof. Babak Falsafi, EPFL
01:00:05
"Regularized information geometric and optimal transport distances between covariance operators..."
01:21:55
âFederated Learning at Scaleâ Prof. Mike Rabbat, Meta AI
01:44:28
âModel Pruning and the Hunt for Lottery Ticketsâ Prof. Dimitris Papailiopoulos
42:15
Panel on Edge AI Algorithms - CIS Edge AI Summer School 2022
48:41
âDesign Methodologies for Flexible, Robust, and Energy-Efficient Edge AIâ Part.I
01:44:03
âPULP: Embedding AI at the Extreme Edge of the IoTâ Prof. Davide Rossi
01:26:31
Panel on Embedded AI Architectures - CIS Edge AI Summer School
44:49
âFrom research to industry: Artificial Intelligence for Industry & Society.â Prof. François Terrier
01:03:55
Panel on edge AI applications - CIS Edge AI Summer School
55:08
âThe Future of Hardware Technologies for Computing N3XT 3D MOSAIC, Illusion Scaleup, Co-designâ
01:36:39
âMemory-Centric Computing â Prof. Onur Mutlu, ETHZ
53:18
âLearning for safety and coordination in uncertain dynamical systemsâ Prof. Maryam Kamgarpour
04:39
Meet the People Behind CIS: Boi Faltings
04:51
Meet the People Behind CIS: James Larus
03:35
Meet the People Behind CIS: Prof. Jan Hesthaven
49:12
"Machine Learning Technologies for Disaster Resilience" Prof. Naonori Ueda
45:08
âFrom Integrative Structural Biology to Bioengineeringâ Prof. Matteo Dal Peraro
58:47
"Insights on gradient-based algorithms in high-dimensional non-convex optimisation" Lenka Zdeborova
43:51
âImproving Robot Design: Data-Driven Approaches to Design & Fabricationâ Prof. Josie Hughes
01:07:41
"The Bayesian Learning Rule for Adaptive AI" Prof. Emtiyaz Khan
41:46
"Photon counting cameras for quantum imaging applications" Prof. Edoardo Charbon
58:08
"Optimal Transport for Statistics and Machine Learning" Prof. Philippe Rigollet, MIT
47:50
"Personal AI to Maximize the Value of Personal Data while Defending Human Rights and Democracy"
47:43
"Neuro-symbolic Scaffolds for Commonsense Representation and Reasoning" Prof. Antoine Bosselut
01:00:59
"Mean-Field Langevin Dynamics: convergence and applications" Prof. LĂ©naĂŻc Chizat
54:49
"Selective Inference for Deep Learning Model-driven Hypotheses" Prof. Ichiro Takeuchi
49:44
"Efficient Machine Learning with Tensor Networks" Prof. Qibin Zhao - EPFL CIS RIKEN AIP
59:25
"New representer theorems for inverse problems and machine learning" Prof. Michaël Unser
01:05:24
"Representing atoms clouds: the foundations of atomic-scale machine learning" Prof. Michele Ceriotti
01:01:23
"Selection bias may be adjusted when the sample size is negative in hierarchical..."Prof. Shimodaira
01:07:00
"Optimization theories of neural networks with its statistical perspective"Prof. Taiji Suzuki
59:28
"Learning with Strange Gradients" Prof. Martin Jaggi - EPFL CIS RIKEN AIP
01:04:30
"Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity" Flammarion
01:04:59
"Robust machine learning for reliable deployment" Prof. Masashi Sugiyama - EPFL CIS RIKEN AIP
39:45
âMicrostructure imaging by diffusion MRI: modeling, simulation, machine learning, application to...
01:19:47
"Optimization challenges in adversarial machine learning" Prof. Volkan Cevher - EPFL CIS RIKEN AIP
42:24
"The case for diversifying your mathematical toolbox" Prof. Kathryn Hess Bellwald
47:48
"JPEG AI: The next generation of learning based image compression" Prof. Touradj Ebrahimi,
43:42
âMulti-individual pose estimation, identification and trackingâ Prof. Alexander Mathis,
46:28
"Deep Surface Meshesâ Prof. Pascal Fua (GTKYN)
01:06:29
Prof. Susan Murphy "We used Reinforcement Learning; but did it work?"
X
About
Center for Intelligent Systems (CIS)
The EPFL Center for Intelligent Systems brings together researchers working on various aspects of creating intelligent systems.
The Center for Intelligent Systems (CIS) at EPFL, is a joint initiative of the School of Architecture Civil and Environmental
Engineering (ENAC), the School of Computer and Communication Sciences (IC), the School of Basic Sciences (SB), the Schools of Engineering (STI) and the School of Life Sciences (SV) that seeks to advance research and practice in the strategic field of intelligent systems. Such systems are embedded with elements of artificial intelligence and are emerging in response to the convergence of the virtual and physical worlds through the development of autonomous systems, wearable technologies, robotic co-workers, smart devices, drone technology, augmented reality, intelligent houses, digital twins, intelligent manufacturing, and many other applications.
Tags