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Found 21 papers, 13 papers with code
Adversarially Balanced Representation for Continuous Treatment Effect Estimation
Individual treatment effect (ITE) estimation requires adjusting for the covariate shift between populations with different treatments, and deep representation learning has shown great promise in learning a balanced representation of covariates.
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design
Searching the vast chemical space for drug-like and synthesizable molecules with high binding affinity to a protein pocket is a challenging task in drug discovery.
Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction
We propose SeMole, a method that augments the Junction Tree Variational Autoencoders, a state-of-the-art generative model for molecular graphs, with semi-supervised learning.
Subgroup Discovery in Unstructured Data
Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest.
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
Causal Inference from Small High-dimensional Datasets
Our experiments show that such an approach helps to bring stability to neural network-based methods and improve the treatment effect estimates in small high-dimensional datasets.
Multi-treatment Effect Estimation from Biomedical Data
This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments.
An Interactive Visualization Tool for Understanding Active Learning
Despite recent progress in artificial intelligence and machine learning, many state-of-the-art methods suffer from a lack of explainability and transparency.
Neural Disjunctive Normal Form: Vertically Integrating Logic With Deep Learning For Classification
We present Neural Disjunctive Normal Form (Neural DNF), a hybrid neuro- symbolic classifier that vertically integrates propositional logic with a deep neural network.
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
Combining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification
The goal of few-shot classification is to learn a model that can classify novel classes using only a few training examples.
Domain Generalization via Semi-supervised Meta Learning
To learn a domain-invariant representation, it also utilizes a novel alignment loss to ensure that the distance between pairs of class centroids, computed after adding the unlabeled samples, is preserved across different domains.
CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data
We study the problem of applying spectral clustering to cluster multi-scale data, which is data whose clusters are of various sizes and densities.
ParKCa: Causal Inference with Partially Known Causes
Methods for causal inference from observational data are an alternative for scenarios where collecting counterfactual data or realizing a randomized experiment is not possible.
Hierarchical Graph Pooling with Structure Learning
HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.
Ranked #1 on
Graph Classification
on PROTEINS
An Active Approach for Model Interpretation
However, we argue that this paradigm is suboptimal for it does not utilize the unique property of the model interpretation problem, that is, the ability to generate synthetic instances and query the target classifier for their labels.
Adversarial Inductive Transfer Learning with input and output space adaptation
To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies.
Checking Functional Modularity in DNN By Biclustering Task-specific Hidden Neurons
While real brain networks exhibit functional modularity, we investigate whether functional mod- ularity also exists in Deep Neural Networks (DNN) trained through back-propagation.
PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction
In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients.
Detecting Singleton Review Spammers Using Semantic Similarity
The second method is based on topic modeling and exploits the similarity of the reviews topic distributions using two models: bag-of-words and bag-of-opinion-phrases.
