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Found 24 papers, 10 papers with code
Vision-and-Language Pretraining
Inheriting the spirit of Transfer Learning, research works in V&L have devised multiple pretraining techniques on large-scale datasets in order to enhance the performance of downstream tasks.
Long Range Graph Benchmark
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm exchange information between 1-hop neighbors to build node representations at each layer.
Ranked #1 on
Node Classification
on PascalVOC-SP
Recipe for a General, Powerful, Scalable Graph Transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.
Ranked #1 on
Graph Regression
on ZINC-500k
Certified Robustness Against Natural Language Attacks by Causal Intervention
Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples.
Contrastive Learning for Neural Topic Model
Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by differentiating a document with another dissimilar sample.
Graph Neural Networks with Learnable Structural and Positional Representations
An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers.
Ranked #5 on
Graph Regression
on ZINC-500k
Grounding Language Representation with Visual Object Information via Cross Modal Pretraining
However, this approach has two main drawbacks: (i) the whole image usually contains more objects and backgrounds than the sentence itself; thus, matching them together will confuse the grounded model; (ii) CNN only extracts the features of the image but not the relationship between objects inside that, limiting the grounded model to learn complicated contexts.
Enriching and Controlling Global Semantics for Text Summarization
Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries.
Towards Robustness Against Natural Language Word Substitutions
Robustness against word substitutions has a well-defined and widely acceptable form, i. e., using semantically similar words as substitutions, and thus it is considered as a fundamental stepping-stone towards broader robustness in natural language processing.
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters
Recent works have demonstrated reasonable success of representation learning in hypercomplex space.
Parameterization of Hypercomplex Multiplications
Recent works have demonstrated reasonable success of representation learning in hypercomplex space.
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences
Understanding human preferences, along with cultural and social nuances, lives at the heart of natural language understanding.
Benchmarking Graph Neural Networks
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.
Ranked #1 on
Link Prediction
on COLLAB
Compositional De-Attention Networks
Attentional models are distinctly characterized by their ability to learn relative importance, i. e., assigning a different weight to input values.
Holographic Factorization Machines for Recommendation
Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks.
Holistic Multi-modal Memory Network for Movie Question Answering
In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop.
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension
Sequence encoders are crucial components in many neural architectures for learning to read and comprehend.
Ranked #7 on
Question Answering
on NarrativeQA
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources.
CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation
Our approach, the CoupleNet is an end-to-end deep learning based estimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users.
Learning to Attend via Word-Aspect Associative Fusion for Aspect-based Sentiment Analysis
Our novel model, \textit{Aspect Fusion LSTM} (AF-LSTM) learns to attend based on associative relationships between sentence words and aspect which allows our model to adaptively focus on the correct words given an aspect term.
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
Our model, LRML (\textit{Latent Relational Metric Learning}) is a novel metric learning approach for recommendation.
Ranked #1 on
Recommendation Systems
on Netflix
(nDCG@10 metric)
