563 citations · 1.3k across the 16 of their papers we have counts for
19 papers
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection
Daniele Comi, Dimitrios Christofidellis, Pier Francesco Piazza +1
Intent discovery is a crucial task in natural language processing, and it is increasingly relevant for various of industrial applications. Identifying novel, unseen intents from us…
Multitask Prompted Training Enables Zero-Shot Task Generalization
Victor Sanh, Albert Webson, Colin Raffel +38
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a…
Understood in Translation, Transformers for Domain Understanding
Dimitrios Christofidellis, Matteo Manica, Leonidas Georgopoulos +1
Knowledge acquisition is the essential first step of any Knowledge Graph (KG) application. This knowledge can be extracted from a given corpus (KG generation process) or specified…
Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks
Modestas Filipavicius, Matteo Manica, Joris Cadow +1
Less than 1% of protein sequences are structurally and functionally annotated. Natural Language Processing (NLP) community has recently embraced self-supervised learning as a power…
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog
Emile Chapuis, Pierre Colombo, Matteo Manica +2
Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic…
PaccMann on SARS-CoV-2: Designing antiviral candidates with conditional generative models
Jannis Born, Matteo Manica, Joris Cadow +4
With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. Bridging systems bio…
Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue
Pierre Colombo, Emile Chapuis, Matteo Manica +3
The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise…
Guiding attention in Sequence-to-sequence models for Dialogue Act prediction
Pierre Colombo, Emile Chapuis, Matteo Manica +3
The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise…
PaccMann: Designing anticancer drugs from transcriptomic data via reinforcement learning
Jannis Born, Matteo Manica, Ali Oskooei +3
With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep le…
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
Matteo Manica, Christoph Auer, Valery Weber +9
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
Matteo Manica, Ali Oskooei, Jannis Born +3
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…
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Atin Sood, Benjamin Elder, Benjamin Herta +17
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through AP…
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Ali Oskooei, Jannis Born, Matteo Manica +3
We present a novel approach for the prediction of anticancer compound sensitivity by means of multi-modal attention-based neural networks (PaccMann). In our approach, we integrate…
Free sequences in P(ω)/fin
David Chodounský, Vera Fischer, Jan Grebík
We investigate maximal free sequences in the Boolean algebra , as defined by D. Monk. We provide some information on the general structure of these obj…
PIMKL: Pathway Induced Multiple Kernel Learning
Matteo Manica, Joris Cadow, Roland Mathis +1
Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification conti…
INtERAcT: Interaction Network Inference from Vector Representations of Words
Matteo Manica, Roland Mathis, María Rodríguez Martínez
In recent years, the number of biomedical publications has steadfastly grown, resulting in a rich source of untapped new knowledge. Most biomedical facts are however not readily av…
Inferring clonal composition from multiple tumor biopsies
Matteo Manica, Hyunjae Ryan Kim, Roland Mathis +14
Explicit accounting for copy number alterations can dramatically improve mutation frequency estimates, leading to more accurate phylogeny reconstructions and subclone characterizat…
Mixed-Precision In-Memory Computing
Manuel Le Gallo, Abu Sebastian, Roland Mathis +6
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 t…