Search Results for author:
Found 17 papers, 7 papers with code
Cleora: A Simple, Strong and Scalable Graph Embedding Scheme
We show that Cleora learns a data abstraction that is similar to contrastive methods, yet at much lower computational cost.
Ranked #2 on
Node Classification
on YouTube
On the Unreasonable Effectiveness of Centroids in Image Retrieval
We propose centroid training and retrieval as a viable method for both Fashion Retrieval and ReID applications.
Ranked #1 on
Image Retrieval
on Exact Street2Shop
An efficient manifold density estimator for all recommendation systems
Many unsupervised representation learning methods belong to the class of similarity learning models.
Ranked #1 on
Session-Based Recommendations
on yoochoose1
(using extra training data)
Modeling Multi-Destination Trips with Sketch-Based Model
The recently proposed EMDE (Efficient Manifold Density Estimator) model achieves state of-the-art results in session-based recommendation.
Synerise at RecSys 2021: Twitter user engagement prediction with a fast neural model
Each data point contains multiple sources of information, such as tweet text along with engagement features, user features, and tweet features.
Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems
In addition, we have created and published a new dataset that may be used for validation of robustness of a Q&A model.
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System
In this paper we present the results of an investigation of the importance of verbs in a deep learning QA system trained on SQuAD dataset.
How much should you ask? On the question structure in QA systems
Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner.
How much should you ask? On the question structure in QA systems.
Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner.
Can Your Context-Aware MT System Pass the DiP Benchmark Tests? : Evaluation Benchmarks for Discourse Phenomena in Machine Translation
Despite increasing instances of machine translation (MT) systems including contextual information, the evidence for translation quality improvement is sparse, especially for discourse phenomena.
Multi-modal Embedding Fusion-based Recommender
Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively.
I know why you like this movie: Interpretable Efficient Multimodal Recommender
Recently, the Efficient Manifold Density Estimator (EMDE) model has been introduced.
DiP Benchmark Tests: Evaluation Benchmarks for Discourse Phenomena in MT
Despite increasing instances of machine translation (MT) systems including extrasentential context information, the evidence for translation quality improvement is sparse, especially for discourse phenomena.
T-EMDE: Sketching-based global similarity for cross-modal retrieval
However, each modality embeddings stem from non-related feature spaces, which causes the notorious 'heterogeneity gap'.
Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations
Motivated by this observation, we introduce a novel visual probing framework for explaining the self-supervised models by leveraging probing tasks employed previously in natural language processing.
Multidimensional Hopfield Networks for clustering
Motivated by these findings we provide a generalisation of Newmans method to the multidimensional case.
