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Found 18 papers, 12 papers with code
Variational Flow Models: Flowing in Your Style
We introduce "posterior flows" - generalizations of "probability flows" to a broader class of stochastic processes not necessarily diffusion processes - and propose a systematic training-free method to transform the posterior flow of a "linear" stochastic process characterized by the equation Xt = at * X0 + st * X1 into a straight constant-speed (SC) flow, reminiscent of Rectified Flow.
Root Cause Explanation of Outliers under Noisy Mechanisms
We then represent the functional form of a target outlier leaf as a function of the node and edge noises.
Robust Estimation of Causal Heteroscedastic Noise Models
One solution to this problem is assuming that cause and effect are generated from a structural causal model, enabling identification of the causal direction after estimating the model in each direction.
Domain Generalisation via Risk Distribution Matching
Hence, we propose a compelling proposition: Minimising the divergences between risk distributions across training domains leads to robust invariance for DG.
Differentiable Bayesian Structure Learning with Acyclicity Assurance
Score-based approaches in the structure learning task are thriving because of their scalability.
Heteroscedastic Causal Structure Learning
The result is HOST (Heteroscedastic causal STructure learning), a simple yet effective causal structure learning algorithm that scales polynomially in both sample size and dimensionality.
Causal Inference via Style Transfer for Out-of-distribution Generalisation
A well-known existing causal inference method like back-door adjustment cannot be applied to remove spurious correlations as it requires the observation of confounders.
Diffeomorphic Information Neural Estimation
Mutual Information (MI) and Conditional Mutual Information (CMI) are multi-purpose tools from information theory that are able to naturally measure the statistical dependencies between random variables, thus they are usually of central interest in several statistical and machine learning tasks, such as conditional independence testing and representation learning.
Conditional Independence Testing via Latent Representation Learning
Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms.
Efficient Classification with Counterfactual Reasoning and Active Learning
Data augmentation is one of the most successful techniques to improve the classification accuracy of machine learning models in computer vision.
Learning to Discover Medicines
Discovering new medicines is the hallmark of human endeavor to live a better and longer life.
Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring
While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction.
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction
Methods: We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex.
GEFA: Early Fusion Approach in Drug-Target Affinity Prediction
In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets.
Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition
Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area.
GraphDTA: prediction of drug–target binding affinity using graph convolutional networks
The results show that our proposed method can not only predict the affinity better than non-deep learning models, but also outperform competing deep learning approaches.
Ranked #4 on
Drug Discovery
on KIBA
Variational Memory Encoder-Decoder
Introducing variability while maintaining coherence is a core task in learning to generate utterances in conversation.
Attentional Multilabel Learning over Graphs: A Message Passing Approach
GAML regards labels as auxiliary nodes and models them in conjunction with the input graph.
