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Found 23 papers, 13 papers with code
Improving Transformer Optimization Through Better Initialization
As Transformer models are becoming larger and more expensive to train, recent research has focused on understanding and improving optimization in these models.
Improving Transformer Optimization Through Better Initialization
As Transformer models are becoming larger and more expensive to train, recent research has focused on understanding and improving optimization in these models.
Data-Efficient Multimodal Fusion on a Single GPU
The goal of multimodal alignment is to learn a single latent space that is shared between multimodal inputs.
MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting
Transformer-based models have greatly pushed the boundaries of time series forecasting recently.
Self-supervised Representation Learning From Random Data Projectors
Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations.
DuETT: Dual Event Time Transformer for Electronic Health Records
Electronic health records (EHRs) recorded in hospital settings typically contain a wide range of numeric time series data that is characterized by high sparsity and irregular observations.
DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation
The student is optimized to predict the output of the teacher after multiple decoding steps while the teacher follows the student via a slow-moving average.
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
Instead, texts often capture sub-regions of entire videos and are most semantically similar to certain frames within videos.
Ranked #17 on
Video Retrieval
on LSMDC
(using extra training data)
Decentralized Federated Learning through Proxy Model Sharing
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing.
Improving Non-Autoregressive Translation Models Without Distillation
Empirically, we show that CMLMC achieves state-of-the-art NAR performance when trained on raw data without distillation and approaches AR performance on multiple datasets.
Temporal Dependencies in Feature Importance for Time Series Predictions
Time series data introduces two key challenges for explainability methods: firstly, observations of the same feature over subsequent time steps are not independent, and secondly, the same feature can have varying importance to model predictions over time.
Weakly Supervised Action Selection Learning in Video
A common approach is to train a frame-level classifier where frames with the highest class probability are selected to make a video-level prediction.
Ranked #4 on
Weakly Supervised Action Localization
on FineAction
Context-Aware Scene Graph Generation With Seq2Seq Transformers
In this task, the model needs to detect objects and predict visual relationships between them.
Learning Effective Visual Relationship Detector on 1 GPU
We present our winning solution to the Open Images 2019 Visual Relationship challenge.
Guided Similarity Separation for Image Retrieval
Despite recent progress in computer vision, image retrieval remains a challenging open problem.
Cross-Class Relevance Learning for Temporal Concept Localization
We present a novel Cross-Class Relevance Learning approach for the task of temporal concept localization.
Semi-Supervised Exploration in Image Retrieval
We present our solution to Landmark Image Retrieval Challenge 2019.
Diabetes Mellitus Forecasting Using Population Health Data in Ontario, Canada
We perform one of the first large-scale machine learning studies with this data to study the task of predicting diabetes in a range of 1-10 years ahead, which requires no additional screening of individuals. In the best setup, we reach a test AUC of 80. 3 with a single-model trained on an observation window of 5 years with a one-year buffer using all datasets.
DropoutNet: Addressing Cold Start in Recommender Systems
Latent models have become the default choice for recommender systems due to their performance and scalability.
Unsupervised Document Embedding With CNNs
We propose a new model for unsupervised document embedding.
Efficient Sampling for Bipartite Matching Problems
Bipartite matching problems characterize many situations, ranging from ranking in information retrieval to correspondence in vision.
Collaborative Ranking With 17 Parameters
The primary application of collaborate filtering (CF) is to recommend a small set of items to a user, which entails ranking.
