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Found 19 papers, 12 papers with code
YerevaNN’s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs
This report describes YerevaNN’s neural machine translation systems and data processing pipelines developed for WMT20 biomedical translation task.
Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction
Vision Transformers (ViTs) have demonstrated remarkable success in achieving state-of-the-art performance across various image-based tasks and beyond.
In-context Learning in Presence of Spurious Correlations
We show that it is possible to obtain an in-context learner that generalizes to unseen tasks by training on a diverse dataset of synthetic in-context learning instances.
Small Molecule Optimization with Large Language Models
Recent advancements in large language models have opened new possibilities for generative molecular drug design.
Outdoor Environment Reconstruction with Deep Learning on Radio Propagation Paths
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, and high computational and energy demands.
Analyzing Local Representations of Self-supervised Vision Transformers
In this paper, we present a comparative analysis of various self-supervised Vision Transformers (ViTs), focusing on their local representative power.
Identifying and Disentangling Spurious Features in Pretrained Image Representations
Neural networks employ spurious correlations in their predictions, resulting in decreased performance when these correlations do not hold.
ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation
The challenging problem of non-line-of-sight (NLOS) localization is critical for many wireless networking applications.
BARTSmiles: Generative Masked Language Models for Molecular Representations
We then quantitatively show that when applied to the molecular domain, the BART objective learns representations that implicitly encode our downstream tasks of interest.
Failure Modes of Domain Generalization Algorithms
On Camelyon-17, domain-invariance degrades the quality of representations on unseen domains.
WARP: Word-level Adversarial ReProgramming
Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks.
Robust Classification under Class-Dependent Domain Shift
Investigation of machine learning algorithms robust to changes between the training and test distributions is an active area of research.
Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
BioRelEx 1.0: Biological Relation Extraction Benchmark
Automatic extraction of relations and interactions between biological entities from scientific literature remains an extremely challenging problem in biomedical information extraction and natural language processing in general.
Analysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine learning.
Efficient Covariance Estimation from Temporal Data
Estimating the covariance structure of multivariate time series is a fundamental problem with a wide-range of real-world applications -- from financial modeling to fMRI analysis.
Towards JointUD: Part-of-speech Tagging and Lemmatization using Recurrent Neural Networks
This paper describes our submission to CoNLL 2018 UD Shared Task.
Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report
We have tried to reproduce the results of the paper "Natural Language Inference over Interaction Space" submitted to ICLR 2018 conference as part of the ICLR 2018 Reproducibility Challenge.
Multitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine learning.
