Deep Learning Extravaganza 2024
Celebrating the most cutting-edge machine learning research of the Amsterdam AI Ecosystem
- Date
June 14, 2024
- Time
16:00-19:30
- Location
Lab42, Amsterdam Science Park
The ELLIS unit Amsterdam is proud to announce the Deep Learning Extravaganza in collaboration with the Deep Thinking Hour at Lab42. Attend the event to connect with Amsterdam’s innovators and explore the latest cutting-edge Machine Learning research.
Event programme
16:10-17:00
Keynote Presentation
Megan Stanley (Microsoft Research Lab)
Venue: L1.02
17:00-19:30
Poster Session
with bites & drinks
Venue: ground floor
Megan Stanley
Senior Researcher
- Keynote speaker
About the keynote speaker
She is a Senior Researcher in the Machine Intelligence group with a focus on methods applied to natural sciences and drug-discovery. Her interests include meta-learning and generative models. She previously completed a PhD at the University of Cambridge at the intersection between Quantum Optics and Condensed Matter.
Aurora, the first large-scale foundation model of the atmosphere
Posters showcased at the event
The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation
- Evgeniia Tokarchuk
- Vlad Niculae
- Nicu Sebe
- Max Welling
CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning
- Andreas W. M. Sauter
- Nicolò Botteghi
- Erman Acar
- Aske Plaat
Neural Diffusion Models
- Jacobus Martin Smit
- Fernando P. Santos
Don’t Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Models
- Anna Bavaresco
- Alberto Testoni
- Raquel Fernández
A Machine with Short-Term, Episodic, and Semantic Memory Systems
- Taewoon Kim
- Michael Cochez
- Vincent François-Lavet
- Mark Neerincx
- Piek Vossen
AAAI 2023
How to Train Neural Field Representations: A Comprehensive Study and Benchmark
- Samuele Papa
- Riccardo Valperga
- David Knigge
- Miltiadis Kofinas
- Phillip Lippe
- Jan-Jakob Sonke
- Efstratios Gavves
CVPR 2024
Learning in Public Goods Games with Non-Linear Utilities: a Multi-Objective Approach
- Nicole Orzan
- Erman Acar
- Davide Grossi
- Roxana Radulescu
ALA 2024
Clifford-Steerable Convolutional Neural Networks
- Maksim Zhdanov
- David Ruhe
- Maurice Weiler
- Ana Lucic
- Johannes Brandstetter
- Patrick Forré
Learning Fair Cooperation in Mixed-Motive Games with Indirect Reciprocity
- Jacobus Martin Smit
- Fernando P. Santos
Emergent Cooperation under Uncertain Incentive Alignment
- Nicole Orzan
- Erman Acar
- Davide Grossi
- Roxana Rădulescu
A Sparsity Principle for Partially Observable Causal Representation Learning
- Danru Xu
- Dingling Yao
- Sébastien Lachapelle
- Perouz Taslakian
- Julius von Kügelgen
- Francesco Locatello
- Sara Magliacane
ICML 2024
Any-Shift Prompting for Generalization over Distributions
- Zehao Xiao
- Jiayi Shen
- Mohammad Mahdi Derakhshani
- Shengcai Liao
- Cees G. M. Snoek
CVPR 2024
Compositional Entailment Learning for Hyperbolic Vision-Language Models
- Avik Pal
- Max van Spengler
- Guido D’Amely
- Alessandro Flaborea
- Fabio Galasso
- Pascal Mettes
Ai-Sampler: Adversarial Learning of Markov kernels with involutive maps
- Evgenii Egorov
- Ricardo Valperga
- Efstratios Gavves
ICML 2024
Continual hyperbolic learning of instances and classes
- Melika Ayoughi
- Mina Ghadimiatigh
- Mohammad Mehdi Derakhshani
- Cees Snoek
- Paul Groth
- Pascal Mettes
DNA: Differentially private Neural Augmentation for contact tracing
- Rob Romijnders
- Christos Louizos
- Yuki M. Asano
- Max Welling
CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research
- Andreas W. M. Sauter
- Erman Acar
- Aske Plaat
DIBS: Enhancing Dense Video Captioning with Unlabeled Videos via Pseudo Boundary Enrichment and Online Refinement
- Hao Wu
- Huabin Liu
- Yu Qiao
- Xiao Sun
CVPR 2024 (main track)
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
- Erik Arakelyan
- Pasquale Minervini
- Daniel Daza
- Michael Cochez
- Isabelle Augenstein
NeurIPS 2023
VeRA: Vector-based Random Matrix Adaptation
- Dawid J. Kopiczko
- Tijmen Blankevoort
- Yuki M. Asano
A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs
- Lars Veefkind
- Gabriele Cesa
ICML 2024 (main track)
CompFuser: Enabling Spatial Composition in Text-to-Image Generation
- Mohammad Mahdi Derakhshani
- Menglin Xia
- Harkirat Behl
- Cees G. M. Snoek
- Victor Rühle
ELLIS unit Amsterdam is proud to announce the Deep Learning Extravaganza on June 13th at LAB42.