75 citations · 231 across the 12 of their papers we have counts for
12 papers
Synth: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings
Sahand Sharifzadeh, Christos Kaplanis, Shreya Pathak +5
The creation of high-quality human-labeled image-caption datasets presents a significant bottleneck in the development of Visual-Language Models (VLMs). In this work, we investigat…
Neural Algorithmic Reasoning with Causal Regularisation
Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz +5
Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of neural networks, effectively demonstrating they can learn to execute classical algorithms…
SemPPL: Predicting pseudo-labels for better contrastive representations
Matko Bošnjak, Pierre H. Richemond, Nenad Tomasev +7
Learning from large amounts of unsupervised data and a small amount of supervision is an important open problem in computer vision. We propose a new semi-supervised learning method…
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Nenad Tomasev, Ioana Bica, Brian McWilliams +4
Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classificatio…
CoBERL: Contrastive BERT for Reinforcement Learning
Andrea Banino, Adrià Puidomenech Badia, Jacob Walker +3
Many reinforcement learning (RL) agents require a large amount of experience to solve tasks. We propose Contrastive BERT for RL (CoBERL), an agent that combines a new contrastive l…
Representation Learning via Invariant Causal Mechanisms
Jovana Mitrovic, Brian McWilliams, Jacob Walker +2
Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combin…
Amortized learning of neural causal representations
Nan Rosemary Ke, Jane. X. Wang, Jovana Mitrovic +2
Causal models can compactly and efficiently encode the data-generating process under all interventions and hence may generalize better under changes in distribution. These models a…
Causally Correct Partial Models for Reinforcement Learning
Danilo J. Rezende, Ivo Danihelka, George Papamakarios +11
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can b…
Causal Reasoning from Meta-reinforcement Learning
Ishita Dasgupta, Jane Wang, Silvia Chiappa +7
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether causal reasoning can emerge via meta-reinf…
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Discovering the causal structure among a set of variables is a fundamental problem in many areas of science. In this paper, we propose Kernel Conditional Deviance for Causal Infere…
Hierarchical Adversarially Learned Inference
Mohamed Ishmael Belghazi, Sai Rajeswar, Olivier Mastropietro +3
We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative and inference model are trained using th…
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate B…