25.8k citations · 55.9k across the 29 of their papers we have counts for
38 papers
Preserving Semantics in Textual Adversarial Attacks
David Herel, Hugo Cisneros, Tomas Mikolov
The growth of hateful online content, or hate speech, has been associated with a global increase in violent crimes against minorities [23]. Harmful online content can be produced e…
Benchmarking Learning Efficiency in Deep Reservoir Computing
Hugo Cisneros, Josef Sivic, Tomas Mikolov
It is common to evaluate the performance of a machine learning model by measuring its predictive power on a test dataset. This approach favors complicated models that can smoothly…
Emergence of Novelty in Evolutionary Algorithms
David Herel, Dominika Zogatova, Matej Kripner +1
One of the main problems of evolutionary algorithms is the convergence of the population to local minima. In this paper, we explore techniques that can avoid this problem by encour…
Classification of Discrete Dynamical Systems Based on Transients
Barbora Hudcová, Tomáš Mikolov
In order to develop systems capable of artificial evolution, we need to identify which systems can produce complex behavior. We present a novel classification method applicable to…
Computational Hierarchy of Elementary Cellular Automata
Barbora Hudcová, Tomáš Mikolov
The complexity of cellular automata is traditionally measured by their computational capacity. However, it is difficult to choose a challenging set of computational tasks suitable…
Visualizing computation in large-scale cellular automata
Hugo Cisneros, Josef Sivic, Tomas Mikolov
Emergent processes in complex systems such as cellular automata can perform computations of increasing complexity, and could possibly lead to artificial evolution. Such a feat woul…
Emergence of Self-Reproducing Metabolisms as Recursive Algorithms in an Artificial Chemistry
Germán Kruszewski, Tomas Mikolov
One of the main goals of Artificial Life is to research the conditions for the emergence of life, not necessarily as it is, but as it could be. Artificial Chemistries are one of th…
Classification of Complex Systems Based on Transients
Barbora Hudcova, Tomas Mikolov
In order to develop systems capable of modeling artificial life, we need to identify, which systems can produce complex behavior. We present a novel classification method applicabl…
Evaluating Online Continual Learning with CALM
Germán Kruszewski, Ionut-Teodor Sorodoc, Tomas Mikolov
Online Continual Learning (OCL) studies learning over a continuous data stream without observing any single example more than once, a setting that is closer to the experience of hu…
Combinatory Chemistry: Towards a Simple Model of Emergent Evolution
Germán Kruszewski, Tomas Mikolov
An explanatory model for the emergence of evolvable units must display emerging structures that (1) preserve themselves in time (2) self-reproduce and (3) tolerate a certain amount…
Evolving Structures in Complex Systems
Hugo Cisneros, Josef Sivic, Tomas Mikolov
In this paper we propose an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata. We discuss several ways how a metric for…
Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment
Piotr Bojanowski, Onur Celebi, Tomas Mikolov +2
In this paper, we focus on the problem of adapting word vector-based models to new textual data. Given a model pre-trained on large reference data, how can we adapt it to a smaller…
Place Deduplication with Embeddings
Carl Yang, Do Huy Hoang, Tomas Mikolov +1
Thanks to the advancing mobile location services, people nowadays can post about places to share visiting experience on-the-go. A large place graph not only helps users explore int…
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
Armand Joulin, Piotr Bojanowski, Tomas Mikolov +2
Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space. Existing works typically solve a le…
Learning Word Vectors for 157 Languages
Edouard Grave, Piotr Bojanowski, Prakhar Gupta +2
Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredien…
Efficient Large-Scale Multi-Modal Classification
D. Kiela, E. Grave, A. Joulin +1
While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal. Here, we examine multi-modal classification where one modality i…
Advances in Pre-Training Distributed Word Representations
Tomas Mikolov, Edouard Grave, Piotr Bojanowski +2
Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl.…
Fast Linear Model for Knowledge Graph Embeddings
Armand Joulin, Edouard Grave, Piotr Bojanowski +2
This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and q…
Learning Simpler Language Models with the Differential State Framework
Alexander G. Ororbia, Tomas Mikolov, David Reitter
Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks such as language modeling. Existing architectures that add…
CommAI: Evaluating the first steps towards a useful general AI
Marco Baroni, Armand Joulin, Allan Jabri +4
With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal. However, most current research focuses inst…
FastText.zip: Compressing text classification models
Armand Joulin, Edouard Grave, Piotr Bojanowski +3
We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory. After considering different solutio…
Variable Computation in Recurrent Neural Networks
Yacine Jernite, Edouard Grave, Armand Joulin +1
Recurrent neural networks (RNNs) have been used extensively and with increasing success to model various types of sequential data. Much of this progress has been achieved through d…
Bag of Tricks for Efficient Text Classification
Armand Joulin, Edouard Grave, Piotr Bojanowski +1
This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifi…
Enriching Word Vectors with Subword Information
Piotr Bojanowski, Edouard Grave, Armand Joulin +1
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the…