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Found 7 papers, 4 papers with code
Explicitly Representing Syntax Improves Sentence-to-layout Prediction of Unexpected Situations
To test compositional understanding, we collect a test set of grammatically correct sentences and layouts describing compositions of entities and relations that unlikely have been seen during training.
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire.
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Crop Classification
on CropHarvest - Kenya
Implicit Temporal Reasoning for Evidence-Based Fact-Checking
Our findings show that the presence of temporal information and the manner in which timelines are constructed greatly influence how fact-checking models determine the relevance and supporting or refuting character of evidence documents.
Optimizing ship detection efficiency in SAR images
The integration of the optimizations with the object detection model leads to a trade-off between speed and performance.
Discrete and continuous representations and processing in deep learning: Looking forward
Motivated by these insights, in this paper we argue that combining discrete and continuous representations and their processing will be essential to build systems that exhibit a general form of intelligence.
Two-phase training mitigates class imbalance for camera trap image classification with CNNs
As a result, they obtain good performance for a few majority classes but poor performance for many minority classes.
Autoregressive Reasoning over Chains of Facts with Transformers
This paper proposes an iterative inference algorithm for multi-hop explanation regeneration, that retrieves relevant factual evidence in the form of text snippets, given a natural language question and its answer.
