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Found 8 papers, 4 papers with code
Solving Physics Puzzles by Reasoning about Paths
We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal.
Entity Linking and Discovery via Arborescence-based Supervised Clustering
Previous work has shown promising results in performing entity linking by measuring not only the affinities between mentions and entities but also those amongst mentions.
From Multimodal to Unimodal Attention in Transformers using Knowledge Distillation
Multimodal Deep Learning has garnered much interest, and transformers have triggered novel approaches, thanks to the cross-attention mechanism.
Multimodal Personality Recognition using Cross-Attention Transformer and Behaviour Encoding
Personality computing and affective computing have gained recent interest in many research areas.
Machine Reading Comprehension using Case-based Reasoning
Given a test question, CBR-MRC first retrieves a set of similar cases from a nonparametric memory and then predicts an answer by selecting the span in the test context that is most similar to the contextualized representations of answers in the retrieved cases.
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within a day -- attributes that are out-of-scope for current KGQA systems.
Data-driven Discovery with Large Generative Models
With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially.
Entity Linking via Explicit Mention-Mention Coreference Modeling
Learning representations of entity mentions is a core component of modern entity linking systems for both candidate generation and making linking predictions.
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