Workshop
NeurIPS 2022 Workshop on Meta-Learning
Huaxiu Yao · Eleni Triantafillou · Fabio Ferreira · Joaquin Vanschoren · Qi Lei
Theater C
Fri 2 Dec, 7 a.m. PST
Recent years have seen rapid progress in meta-learning methods, which transfer knowledge across tasks and domains to efficiently learn new tasks, optimize the learning process itself, and even generate new learning methods from scratch. Meta-learning can be seen as the logical conclusion of the arc that machine learning has undergone in the last decade, from learning classifiers, to learning representations, and finally to learning algorithms that themselves acquire representations, classifiers, and policies for acting in environments. In practice, meta-learning has been shown to yield new state-of-the-art automated machine learning methods, novel deep learning architectures, and substantially improved one-shot learning systems. Moreover, improving one’s own learning capabilities through experience can also be viewed as a hallmark of intelligent beings, and neuroscience shows a strong connection between human and reward learning and the growing sub-field of meta-reinforcement learning.
Some of the fundamental questions that this workshop aims to address are:
- What are the meta-learning processes in nature (e.g., in humans), and how can we take inspiration from them?
- What is the relationship between meta-learning, continual learning, and transfer learning?
- What interactions exist between meta-learning and large pretrained / foundation models?
- What principles can we learn from meta-learning to help us design the next generation of learning systems?
- What kind of theoretical principles can we develop for meta-learning?
- How can we exploit our domain knowledge to effectively guide the meta-learning process and make it more efficient?
- How can we design better benchmarks for different meta-learning scenarios?
As prospective participants, we primarily target machine learning researchers interested in the questions and foci outlined above. Specific target communities within machine learning include, but are not limited to: meta-learning, AutoML, reinforcement learning, deep learning, optimization, evolutionary computation, and Bayesian optimization. We also invite submissions from researchers who study human learning and neuroscience, to provide a broad and interdisciplinary perspective to the attendees.
Schedule
Fri 7:00 a.m. - 7:10 a.m.
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Opening remarks
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Opening remarks
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Fri 7:10 a.m. - 7:40 a.m.
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Invited talk: Mengye Ren
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Invited talk
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Fri 7:40 a.m. - 8:10 a.m.
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Invited talk: Lucas Beyer
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Invited talk
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Fri 8:10 a.m. - 8:25 a.m.
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Contributed Talk 1: FiT: Parameter Efficient Few-shot Transfer Learning
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Contributed Talk
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Fri 8:25 a.m. - 8:40 a.m.
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Break
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Fri 8:40 a.m. - 9:40 a.m.
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Poster session 1 ( poster session ) > link | 🔗 |
Fri 9:40 a.m. - 9:55 a.m.
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Contributed talk 2: Optimistic Meta-Gradients
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contributed talk
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Fri 9:55 a.m. - 10:25 a.m.
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Invited talk: Elena Gribovskaya
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invited talk
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Fri 10:25 a.m. - 12:00 p.m.
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Lunch break
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Fri 12:00 p.m. - 12:30 p.m.
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Invited talk: Chelsea Finn
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invited talk
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Fri 12:30 p.m. - 1:00 p.m.
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Invited talk: Greg Yang
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invited talk
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Fri 1:00 p.m. - 1:15 p.m.
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Contributed talk 3: The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence
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contributed talk
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Fri 1:15 p.m. - 2:15 p.m.
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Poster session 2
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poster session
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Fri 2:15 p.m. - 2:30 p.m.
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Contributed talk 4: HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks
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contributed talk
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SlidesLive Video |
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Fri 2:30 p.m. - 3:00 p.m.
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Invited talk: Percy Liang
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invited talk
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SlidesLive Video |
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Fri 3:00 p.m. - 3:50 p.m.
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Discussion panel
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discussion panel
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Fri 3:50 p.m. - 4:00 p.m.
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Closing remarks
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Closing remarks
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LOTUS: Learning to learn with Optimal Transport in Unsupervised Scenarios
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Poster
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SlidesLive Video |
prabhant singh · Joaquin Vanschoren 🔗 |
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Test-time adaptation with slot-centric models
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Poster
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link
SlidesLive Video |
Mihir Prabhudesai · Sujoy Paul · Sjoerd van Steenkiste · Mehdi S. M. Sajjadi · Anirudh Goyal · Deepak Pathak · Katerina Fragkiadaki · Gaurav Aggarwal · Thomas Kipf 🔗 |
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Meta-Learning Makes a Better Multimodal Few-shot Learner
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Poster
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SlidesLive Video |
Ivona Najdenkoska · Xiantong Zhen · Marcel Worring 🔗 |
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Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
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Poster
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SlidesLive Video |
Steven Adriaensen · Herilalaina Rakotoarison · Samuel Müller · Frank Hutter 🔗 |
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Adversarial Cheap Talk ( Poster ) > link | Chris Lu · Timon Willi · Alistair Letcher · Jakob Foerster 🔗 |
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Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning
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Poster
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SlidesLive Video |
Sanghwan Kim · Lorenzo Noci · Antonio Orvieto · Thomas Hofmann 🔗 |
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Optimistic Meta-Gradients ( Poster ) > link | Sebastian Flennerhag · Tom Zahavy · Brendan O'Donoghue · Hado van Hasselt · András György · Satinder Singh 🔗 |
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Transfer NAS with Meta-learned Bayesian Surrogates
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Poster
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SlidesLive Video |
Gresa Shala · Thomas Elsken · Frank Hutter · Josif Grabocka 🔗 |
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Gray-Box Gaussian Processes for Automated Reinforcement Learning
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Poster
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SlidesLive Video |
Gresa Shala · André Biedenkapp · Frank Hutter · Josif Grabocka 🔗 |
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AutoRL-Bench 1.0
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Poster
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link
SlidesLive Video |
Gresa Shala · Sebastian Pineda Arango · André Biedenkapp · Frank Hutter · Josif Grabocka 🔗 |
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PersA-FL: Personalized Asynchronous Federated Learning ( Poster ) > link | M. Taha Toghani · Soomin Lee · Cesar Uribe 🔗 |
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Bayesian Optimization with a Neural Network Meta-learned on Synthetic Data Only
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Poster
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link
SlidesLive Video |
Samuel Müller · Sebastian Pineda Arango · Matthias Feurer · Josif Grabocka · Frank Hutter 🔗 |
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Recommendation for New Drugs with Limited Prescription Data ( Poster ) > link | Zhenbang Wu · Huaxiu Yao · Zhe Su · David Liebovitz · Lucas Glass · James Zou · Chelsea Finn · Jimeng Sun 🔗 |
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Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
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Poster
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SlidesLive Video |
Carolin Benjamins · Anja Jankovic · Elena Raponi · Koen van der Blom · Marius Lindauer · Carola Doerr 🔗 |
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One-Shot Optimal Design for Gaussian Process Analysis of Randomized Experiments
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Poster
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SlidesLive Video |
Jelena Markovic · Qing Feng · Eytan Bakshy 🔗 |
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Learning to Prioritize Planning Updates in Model-based Reinforcement Learning
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Poster
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SlidesLive Video |
Brad Burega · John Martin · Michael Bowling 🔗 |
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GraViT-E: Gradient-based Vision Transformer Search with Entangled Weights
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Poster
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SlidesLive Video |
Rhea Sukthanker · Arjun Krishnakumar · sharat patil · Frank Hutter 🔗 |
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Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
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Poster
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SlidesLive Video |
Boris Ivanovic · James Harrison · Marco Pavone 🔗 |
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PriorBand: HyperBand + Human Expert Knowledge
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Poster
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link
SlidesLive Video |
Neeratyoy Mallik · Carl Hvarfner · Danny Stoll · Maciej Janowski · Edward Bergman · Marius Lindauer · Luigi Nardi · Frank Hutter 🔗 |
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The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence
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Poster
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link
SlidesLive Video |
Brando Miranda · Patrick Yu · Yu-Xiong Wang · Sanmi Koyejo 🔗 |
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Towards Discovering Neural Architectures from Scratch
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Poster
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link
SlidesLive Video |
Simon Schrodi · Danny Stoll · Robin Ru · Rhea Sukthanker · Thomas Brox · Frank Hutter 🔗 |
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HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks
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Poster
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link
SlidesLive Video |
Filip Szatkowski · Karol J. Piczak · Przemysław Spurek · Jacek Tabor · Tomasz Trzcinski 🔗 |
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On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition
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Poster
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link
SlidesLive Video |
Samuel Dooley · Rhea Sukthanker · John Dickerson · Colin White · Frank Hutter · Micah Goldblum 🔗 |
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Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction
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Poster
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SlidesLive Video |
Sangwoo Park · Kfir M. Cohen · Osvaldo Simeone 🔗 |
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Multi-objective Tree-structured Parzen Estimator Meets Meta-learning
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Poster
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SlidesLive Video |
Shuhei Watanabe · Noor Awad · Masaki Onishi · Frank Hutter 🔗 |
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Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning
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Poster
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link
SlidesLive Video |
Huiwon Jang · Hankook Lee · Jinwoo Shin 🔗 |
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Uncertainty-Aware Meta-Learning for Multimodal Task Distributions
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Poster
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link
SlidesLive Video |
Cesar Almecija · Apoorva Sharma · Young-Jin Park · Navid Azizan 🔗 |
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Lightweight Prompt Learning with General Representation for Rehearsal-free Continual Learning
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Poster
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SlidesLive Video |
Hyunhee Chung · Kyung Ho Park 🔗 |
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Meta-RL for Multi-Agent RL: Learning to Adapt to Evolving Agents
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Poster
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link
SlidesLive Video |
Matthias Gerstgrasser · David Parkes 🔗 |
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Neural Architecture for Online Ensemble Continual Learning
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Poster
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link
SlidesLive Video |
Mateusz Wójcik · Witold Kościukiewicz · Adam Gonczarek · Tomasz Kajdanowicz 🔗 |
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Meta-Learning via Classifier(-free) Guidance
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Poster
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link
SlidesLive Video |
Elvis Nava · Seijin Kobayashi · Yifei Yin · Robert Katzschmann · Benjamin F. Grewe 🔗 |
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MARS: Meta-learning as score matching in the function space
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Poster
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link
SlidesLive Video |
Kruno Lehman · Jonas Rothfuss · Andreas Krause 🔗 |
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Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function
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Poster
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link
SlidesLive Video |
Clément Bonnet · Laurence Midgley · Alexandre Laterre 🔗 |
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GramML: Exploring Context-Free Grammars with Model-Free Reinforcement Learning
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Poster
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link
SlidesLive Video |
Hernan C. Vazquez · Jorge Sanchez · Rafael Carrascosa 🔗 |
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Efficient Queries Transformer Neural Processes
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Poster
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link
SlidesLive Video |
Leo Feng · Hossein Hajimirsadeghi · Yoshua Bengio · Mohamed Osama Ahmed 🔗 |
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Meta-learning of Black-box Solvers Using Deep Reinforcement Learning
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Poster
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link
SlidesLive Video |
Cedric Malherbe · Aladin Virmaux · Ludovic Dos Santos · Sofian Chaybouti 🔗 |
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Contextual Squeeze-and-Excitation
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Poster
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link
SlidesLive Video |
Massimiliano Patacchiola · John Bronskill · Aliaksandra Shysheya · Katja Hofmann · Sebastian Nowozin · Richard Turner 🔗 |
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Conditional Neural Processes for Molecules
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Poster
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link
SlidesLive Video |
Miguel Garcia-Ortegon · Andreas Bender · Sergio Bacallado 🔗 |
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Meta-Learning General-Purpose Learning Algorithms with Transformers
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Poster
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link
SlidesLive Video |
Louis Kirsch · Luke Metz · James Harrison · Jascha Sohl-Dickstein 🔗 |
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Betty: An Automatic Differentiation Library for Multilevel Optimization
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Poster
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link
SlidesLive Video |
Sang Keun Choe · Willie Neiswanger · Pengtao Xie · Eric Xing 🔗 |
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FiT: Parameter Efficient Few-shot Transfer Learning
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Poster
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link
SlidesLive Video |
Aliaksandra Shysheya · John Bronskill · Massimiliano Patacchiola · Sebastian Nowozin · Richard Turner 🔗 |
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Topological Continual Learning with Wasserstein Distance and Barycenter
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Poster
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link
SlidesLive Video |
Tananun Songdechakraiwut · Xiaoshuang Yin · Barry Van Veen 🔗 |
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Multiple Modes for Continual Learning ( Poster ) > link | Siddhartha Datta · Nigel Shadbolt 🔗 |
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Interpolating Compressed Parameter Subspaces ( Poster ) > link | Siddhartha Datta · Nigel Shadbolt 🔗 |
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HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection
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Poster
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link
SlidesLive Video |
Lukas Fehring · Jonas Hanselle · Alexander Tornede 🔗 |