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Found 14 papers, 4 papers with code
Synth$^2$: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings
The creation of high-quality human-labeled image-caption datasets presents a significant bottleneck in the development of Visual-Language Models (VLMs).
Neural Algorithmic Reasoning with Causal Regularisation
We evaluate our method on the CLRS algorithmic reasoning benchmark, where we show up to 3$\times$ improvements on the OOD test data.
SemPPL: Predicting pseudo-labels for better contrastive representations
We propose a new semi-supervised learning method, Semantic Positives via Pseudo-Labels (SemPPL), that combines labelled and unlabelled data to learn informative representations.
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Most notably, ReLICv2 is the first unsupervised representation learning method to consistently outperform the supervised baseline in a like-for-like comparison over a range of ResNet architectures.
Ranked #14 on
Semantic Segmentation
on PASCAL VOC 2012 val
Representation Learning
Self-Supervised Image Classification
+3
CoBERL: Contrastive BERT for Reinforcement Learning
Many reinforcement learning (RL) agents require a large amount of experience to solve tasks.
Less can be more in contrastive learning
Usually the other datapoints in the batch serve as the negatives for the given datapoint.
Representation Learning via Invariant Causal Mechanisms
Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data.
Ranked #77 on
Self-Supervised Image Classification
on ImageNet
Amortized learning of neural causal representations
The CRN represent causal models using continuous representations and hence could scale much better with the number of variables.
Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions.
Infinitely Deep Infinite-Width Networks
Infinite-width neural networks have been extensively used to study the theoretical properties underlying the extraordinary empirical success of standard, finite-width neural networks.
Causal Reasoning from Meta-reinforcement Learning
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents.
Causal Inference via Kernel Deviance Measures
Discovering the causal structure among a set of variables is a fundamental problem in many areas of science.
Hierarchical Adversarially Learned Inference
We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model.
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between the observed and simulated data as measured by a predefined set of summary statistics.
