deep learning extravaganza 2024

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.
The event will take place on June 13th, 16:00-19:30, at Lab42 (Science Park, University of Amsterdam) and will feature a keynote presentation (16:00-17:00) followed by a poster session with drinks and bites (17:00-19:30). Use this link to sign up to the event. 

Megan Stanley,  Microsoft Research Lab

Keynote presenter

Aurora, the first large-scale foundation model of the atmosphere 

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.

Below, you will find an overview over the papers that will be presented at the event. 

presented papers

The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation

Evgeniia Tokarchuk · Vlad Niculae 

NAACL 2024 (main track)

Clifford-Steerable Convolutional Neural Networks

Maksim Zhdanov · David Ruhe · Maurice Weiler · Ana Lucic · Johannes Brandstetter · Patrick Forré

ICML 2024

Continual hyperbolic learning of instances and classes

Melika Ayoughi · Mina Ghadimiatigh · Mohammad Mehdi Derakhshani · Cees Snoek · Paul Groth · Pascal Mettes 

under review (TOMM journal)

DNA: Differentially private Neural Augmentation for contact tracing

Rob Romijnders · Christos Louizos · Yuki M. Asano · Max Welling

ICLR 2024 (Private ML workshop) 

Learning Fair Cooperation in Mixed-Motive Games with Indirect Reciprocity

Jacobus Martin Smit · Fernando P. Santos

IJCAI 2024 (main track)

CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning

Andreas W. M. Sauter ·  Nicolò Botteghi · Erman Acar · Aske Plaat 

AAMAS 2024 (main track)

Emergent Cooperation under Uncertain Incentive Alignment

Nicole Orzan · Erman Acar · Davide Grossi · Roxana Rădulescu

AAMAS 2024 (main track)

CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research

Andreas W. M. Sauter · Erman Acar · Aske Plaat

CVPR 2024 (CORR workshop)

Neural Diffusion Models

Grigory Bartosh · Dmitry Vetrov · Christian A. Naesseth

Don’t Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Models

Anna Bavaresco · Alberto Testoni · Raquel Fernández

ACL 2024 (track: Multimodality and Language Grounding to Vision, Robotics and Beyond)

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

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)

A Machine with Short-Term, Episodic, and Semantic Memory Systems

Taewoon Kim · Michael Cochez ·  Vincent François-Lavet · Mark Neerincx · Piek Vossen 

AAAI 2023

Any-Shift Prompting for Generalization over Distributions

Zehao Xiao · Jiayi Shen ·  Mohammad Mahdi Derakhshani · Shengcai Liao · Cees G. M. Snoek 

CVPR 2024

Adapting Neural Link Predictors for Data-Efficient Complex Query Answering

Erik Arakelyan · Pasquale Minervini · Daniel Daza · Michael Cochez · Isabelle Augenstein 

NeurIPS 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

Compositional Entailment Learning for Hyperbolic Vision-Language Models

Avik Pal · Max van Spengler · Guido D’Amely  · Alessandro Flaborea · Fabio Galasso · Pascal Mettes

VeRA: Vector-based Random Matrix Adaptation

Dawid J. Kopiczko · Tijmen Blankevoort · Yuki M. Asano 

ICLR 2024 (main track)

Ai-Sampler: Adversarial Learning of Markov kernels with involutive maps

Evgenii Egorov · Ricardo Valperga · Efstratios Gavves 

ICML 2024

A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs

Lars Veefkind · Gabriele Cesa 

ICML 2024 (main track)


Learning in Public Goods Games with Non-Linear Utilities: a Multi-Objective Approach

Nicole Orzan · Erman Acar · Davide Grossi · Roxana Radulescu

ALA 2024

CompFuser: Enabling Spatial Composition in Text-to-Image Generation

Mohammad Mahdi Derakhshani ·  Menglin Xia ·  Harkirat Behl · Cees GM Snoek · Victor Rühle