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Found 20 papers, 7 papers with code
Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens
We report the Regression Transformer (RT), a method that abstracts regression as a conditional sequence modeling problem.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
Identification of Enzymatic Active Sites with Unsupervised Language Modeling
Herein, we apply a Transformer architecture to a language representation of bio-catalyzed chemical reactions to learn the signal at the base of the substrate-active site atomic interactions.
On the Importance of Looking at the Manifold
We explore a spectrum of models, ranging from uniquely learning representations based on the isolated features of the nodes (focusing on Variational Autoencoders), to uniquely learning representations based on the topology (using node2vec) passing through models that integrate both node features and topological information in a hybrid fashion.
An Automated Domain Understanding Technique for Knowledge Graph Generation
Initially, the structure of the domain of interest is inferred from the corpus in the form of a metagraph.
Understood in Translation, Transformers for Domain Understanding
Focusing on scientific document understanding, we present a new health domain dataset based on publications extracted from PubMed and we successfully utilize our method on this.
Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks
Less than 1% of protein sequences are structurally and functionally annotated.
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog
We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives.
Dialogue Act Classification
Emotion Recognition in Conversation
+1
PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models
With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents.
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.
Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue
The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents.
Guiding attention in Sequence-to-sequence models for Dialogue Act prediction
The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents.
Ranked #1 on
Dialogue Act Classification
on Switchboard corpus
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning
The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i. e., transcriptomic profile).
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion.
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder.
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Our models ingest a drug-cell pair consisting of SMILES encoding of a compound and the gene expression profile of a cancer cell and predicts an IC50 sensitivity value.
Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer
We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data.
PIMKL: Pathway Induced Multiple Kernel Learning
Reliable identification of molecular biomarkers is essential for accurate patient stratification.
Mixed-Precision In-Memory Computing
As CMOS scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to significantly extend the performance of today's computers.
Emerging Technologies
