Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption electronic health records (EHRs) created an explosion digital clinical available for analysis, but progress learning healthcare research has been difficult to measure because absence publicly benchmark sets. To address this problem, we propose four prediction benchmarks using derived from Medical Information Mart Intensive Care (MIMIC-III) database. These tasks cover a range problems...

Karen Hambardzumyan, Hrant Khachatrian, Jonathan May. Proceedings of the 59th Annual Meeting Association for Computational Linguistics and 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

The success of deep learning in visual recognition tasks has driven advancements multiple fields research. Particularly, increasing attention been drawn towards its application agriculture. Nevertheless, while pattern on farmlands carries enormous economic values, little progress made to merge computer vision and crop sciences due the lack suitable agricultural image datasets. Meanwhile, problems agriculture also pose new challenges vision. For example, semantic segmentation aerial farmland...

We discover a robust self-supervised strategy tailored toward molecular representations for generative masked language models through series of tailored, in-depth ablations. Using this pretraining strategy, we train BARTSmiles, BART-like model with an order magnitude more compute than previous representations. In-depth evaluations show that BARTSmiles consistently outperforms other across classification, regression, and generation tasks, setting new state-of-the-art on eight tasks. then when...

A proper edge-coloring of a graph G with colors 1, . ., t is an interval t-coloring if all are used and the edges incident to each vertex form integers.A colorable it has for some positive integer t.Let N be set graphs.For ∈ N, least greatest values which denoted by w(G) W (G), respectively.In this paper we first show that r-regular and. Next, investigate edge-colorings grids, cylinders tori.In particular, prove H planar both factors have at 3 vertices, then w(G H) ≤ 6.Finally, confirm...

Abstract An edge‐coloring of a graph G with colors is called an interval t ‐coloring if all are used, and the edges incident to any vertex distinct form integers. In 1991, Erdős constructed bipartite 27 vertices maximum degree 13 that has no coloring. Erdős's counterexample smallest (in sense degree) known not colorable. On other hand, in 1992, Hansen showed graphs at most 3 have this article, we give some methods for constructing non‐edge‐colorable graphs. particular, by these methods,...

We discover a robust self-supervised strategy tailored towards molecular representations for generative masked language models through series of tailored, in-depth ablations. Using this pre-training strategy, we train BARTSmiles, BART-like model with an order magnitude more compute than previous representations. In-depth evaluations show that BARTSmiles consistently outperforms other across classification, regression, and generation tasks setting new state-of-the-art on 11 tasks. then...

Collecting labeled data for many important tasks in chemoinformatics is time consuming and requires expensive experiments. In recent years, machine learning has been used to learn rich representations of molecules using large scale unlabeled molecular datasets transfer the knowledge solve more challenging with limited datasets. Variational autoencoders are one tools that have proposed perform both chemical property prediction generation tasks. this work we propose a simple method improve...

Recent advancements in large language models have opened new possibilities for generative molecular drug design. We present Chemlactica and Chemma, two fine-tuned on a novel corpus of 110M molecules with computed properties, totaling 40B tokens. These demonstrate strong performance generating specified properties predicting characteristics from limited samples. introduce optimization algorithm that leverages our to optimize arbitrary given access black box oracle. Our approach combines ideas...

Automatic extraction of relations and interactions between biological entities from scientific literature remains an extremely challenging problem in biomedical information natural language processing general. One the reasons for slow progress is relative scarcity standardized publicly available benchmarks. In this paper we introduce BioRelEx, a new dataset fully annotated sentences that capture binding proteins and/or biomolecules. To foster reproducible research on interaction task, define...

The success of deep learning in visual recognition tasks has driven advancements multiple fields research. Particularly, increasing attention been drawn towards its application agriculture. Nevertheless, while pattern on farmlands carries enormous economic values, little progress made to merge computer vision and crop sciences due the lack suitable agricultural image datasets. Meanwhile, problems agriculture also pose new challenges vision. For example, semantic segmentation aerial farmland...

The challenging problem of non-line-of-sight (NLOS) localization is critical for many wireless networking applications. lack available datasets has made NLOS difficult to tackle with ML-driven methods, but recent developments in synthetic dataset generation have provided new opportunities research. This paper explores three different input representations: (i) single radio path features, (ii) link features (multi-path), and (iii) image-based representations. Inspired by the two latter...

Domain generalization algorithms use training data from multiple domains to learn models that generalize well unseen domains. While recently proposed benchmarks demon-strate most of the existing do not outperform simple baselines, established evaluation methods fail expose impact various factors contribute poor performance. In this paper we propose an framework for domain allows decomposition error into components capturing distinct aspects generalization. Inspired by prevalence based on...

We have tried to reproduce the results of paper "Natural Language Inference over Interaction Space" submitted ICLR 2018 conference as part Reproducibility Challenge. Initially, we were not aware that code was available, so started implement network from scratch. evaluated our version model on Stanford NLI dataset and reached 86.38% accuracy test set, while claims 88.0% accuracy. The main difference, understand it, comes optimizers way selection is performed.

An edge-coloring of a graph G with colors 1,..., t is an interval t-coloring if all are used, and the edges incident to each vertex distinct form integers. A colorable it has for some positive integer t. The set graphs denoted by R. Recently, Toft conjectured that bipartite maximum degree at most 4 colorable. In this paper we prove that: 1) Δ(G) ≤ 4, then G□K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> N R; 2) Δ (G) = 5 without 3, 3)...

This paper describes our submission to CoNLL UD Shared Task 2018. We have extended an LSTM-based neural network designed for sequence tagging additionally generate character-level sequences. The was jointly trained produce lemmas, part-of-speech tags and morphological features. Sentence segmentation, tokenization dependency parsing were handled by UDPipe 1.2 baseline. results demonstrate the viability of proposed multitask architecture, although its performance still remains far from...

An interval t-coloring of a graph G is proper edge-coloring with colors 1,2,...,t such that at least one edge colored by i, i=1,2,...,t, and the edges incident to each vertex v\in V(G) are d_{G}(v) consecutive colors, where degree v in G. In this paper edge-colorings various products investigated.

This paper describes our submission to CoNLL 2018 UD Shared Task. We have extended an LSTM-based neural network designed for sequence tagging additionally generate character-level sequences. The was jointly trained produce lemmas, part-of-speech tags and morphological features. Sentence segmentation, tokenization dependency parsing were handled by UDPipe 1.2 baseline. results demonstrate the viability of proposed multitask architecture, although its performance still remains far from...

Semi-supervised learning is a branch of machine focused on improving the performance models when labeled data scarce, but there access to large number unlabeled examples. Over past five years has been remarkable progress in designing algorithms which are able get reasonable image classification accuracy having labels for only 0.1% samples. In this survey, we describe most recently proposed deep semi-supervised and identify main trends research field. Next, compare several components...

In this paper, we present a comparative analysis of various self-supervised Vision Transformers (ViTs), focusing on their local representative power. Inspired by large language models, examine the abilities ViTs to perform computer vision tasks with little no fine-tuning. We design evaluation framework analyze quality local, i.e.\ patch-level, representations in context few-shot semantic segmentation, instance identification, object retrieval and tracking. discover that contrastive learning...

Conventional methods for outdoor environment reconstruction rely predominantly on vision-based techniques like photogrammetry and LiDAR, facing limitations such as constrained coverage, susceptibility to environmental conditions, high computational energy demands. These challenges are particularly pronounced in applications augmented reality navigation, especially when integrated with wearable devices featuring resources budgets. In response, this paper proposes a novel approach harnessing...