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Found 27 papers, 23 papers with code
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences
While Transformers have yielded impressive results, their quadratic runtime dependency on the sequence length complicates their use for long genomic sequences and in-context learning on proteins and chemical sequences.
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks
In recent years, there has been a trend in the field of Reinforcement Learning (RL) towards large action models trained offline on large-scale datasets via sequence modeling.
xLSTM: Extended Long Short-Term Memory
In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM).
VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
However, the performance of GNNs at binding site identification is still limited potentially due to the lack of dedicated nodes that model hidden geometric entities, such as binding pockets.
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling.
Principled Weight Initialisation for Input-Convex Neural Networks
By studying signal propagation through layers with non-negative weights, we are able to derive a principled weight initialisation for ICNNs.
Context-enriched molecule representations improve few-shot drug discovery
Our novel concept for molecule representation enrichment is to associate molecules from both the support set and the query set with a large set of reference (context) molecules through a Modern Hopfield Network.
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently they have to be trained or fine-tuned for new tasks.
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
We suggest to use modern Hopfield networks to tackle the problem of explaining away.
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials.
MC-LSTM: Mass-Conserving LSTM
MC-LSTMs set a new state-of-the-art for neural arithmetic units at learning arithmetic operations, such as addition tasks, which have a strong conservation law, as the sum is constant over time.
Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales.
Cross-Domain Few-Shot Learning by Representation Fusion
On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.
Hopfield Networks is All You Need
The new update rule is equivalent to the attention mechanism used in transformers.
Modern Hopfield Networks and Attention for Immune Repertoire Classification
We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
The aim of this study is to evaluate whether it is possible to detect basal cell carcinomas in histological sections using attention-based deep learning models and to overcome the ultra-high resolution and the weak labels of whole slide images.
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets
The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone.
End-to-end learning of pharmacological assays from high-resolution microscopy images
Surprisingly, we could predict 29% of the 209 pharmacological assays at high predictive performance (AUC > 0. 9).
Human-level Protein Localization with Convolutional Neural Networks
We present the largest comparison of CNN architectures including GapNet-PL for protein localization in HTI images of human cells.
NeuralHydrology -- Interpreting LSTMs in Hydrology
LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system.
Interpretable Deep Learning in Drug Discovery
Without any means of interpretation, neural networks that predict molecular properties and bioactivities are merely black boxes.
Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery
We propose a novel distance measure between two sets of molecules, called Fr\'echet ChemNet distance (FCD), that can be used as an evaluation metric for generative models.
DeepSynergy: Predicting Anti-Cancer Drug Synergy with Deep Learning
While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space.
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution.
Self-Normalizing Neural Networks
We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.
Ranked #8 on
Drug Discovery
on Tox21
Toxicity Prediction using Deep Learning
The goal of this challenge was to assess the performance of computational methods in predicting the toxicity of chemical compounds.
