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Found 18 papers, 11 papers with code
Learning Decision Trees as Amortized Structure Inference
Building predictive models for tabular data presents fundamental challenges, notably in scaling consistently, i. e., more resources translating to better performance, and generalizing systematically beyond the training data distribution.
Action abstractions for amortized sampling
As trajectories sampled by policies used by reinforcement learning (RL) and generative flow networks (GFlowNets) grow longer, credit assignment and exploration become more challenging, and the long planning horizon hinders mode discovery and generalization.
Cell Morphology-Guided Small Molecule Generation with GFlowNets
We consider an alternative approach in which we leverage an unsupervised multimodal joint embedding to define a latent similarity as a reward for GFlowNets.
RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule, which is a crucial task in molecular discovery.
RGFN: Synthesizable Molecular Generation Using GFlowNets
Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional in silico screening libraries.
Towards DNA-Encoded Library Generation with GFlowNets
DNA-encoded libraries (DELs) are a powerful approach for rapidly screening large numbers of diverse compounds.
Towards equilibrium molecular conformation generation with GFlowNets
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule.
Crystal-GFN: sampling crystals with desirable properties and constraints
Accelerating material discovery holds the potential to greatly help mitigate the climate crisis.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
ChiENN: Embracing Molecular Chirality with Graph Neural Networks
Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics.
Imbalanced data preprocessing techniques utilizing local data characteristics
The focus of this thesis is development of novel data resampling strategies natively utilizing the information about the distribution of both minority and majority class.
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
Our $5\times2$ cross-validated results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR achieves a better precision-recall trade-off than CCR and generally out-performs the state-of-the-art resampling methods in terms of AUC and G-mean.
DiagSet: a dataset for prostate cancer histopathological image classification
Cancer diseases constitute one of the most significant societal challenges.
Potential Anchoring for imbalanced data classification
The results of the experiments conducted on 60 imbalanced datasets show outperformance of Potential Anchoring over state-of-the-art resampling algorithms, including previously proposed methods that utilize radial basis functions to model class potential.
CSMOUTE: Combined Synthetic Oversampling and Undersampling Technique for Imbalanced Data Classification
Furthermore, we combine both in the Combined Synthetic Oversampling and Undersampling Technique (CSMOUTE), which integrates SMOTE oversampling with SMUTE undersampling.
Two-Stage Resampling for Convolutional Neural Network Training in the Imbalanced Colorectal Cancer Image Classification
Data imbalance remains one of the open challenges in the contemporary machine learning.
Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise
The proposed method utilizes an energy-based approach to modeling the regions suitable for oversampling, less affected by small disjuncts and outliers than SMOTE.
Radial-Based Undersampling for Imbalanced Data Classification
Aforementioned difficulty factors can also limit the applicability of some of the methods of dealing with data imbalance, in particular the neighborhood-based oversampling algorithms based on SMOTE.
