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Found 45 papers, 14 papers with code
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly adapt to unseen tasks.
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
Knowledge and language understanding of models evaluated through question answering (QA) has been usually studied on static snapshots of knowledge, like Wikipedia.
Internet-augmented language models through few-shot prompting for open-domain question answering
In this work, we aim to capitalize on the unique few-shot capabilities of large-scale language models (LSLMs) to overcome some of their challenges with respect to grounding to factual and up-to-date information.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
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Abstract Algebra
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Dynamic population-based meta-learning for multi-agent communication with natural language
In this work, our goal is to train agents that can coordinate with seen, unseen as well as human partners in a multi-agent communication environment involving natural language.
Emergent Communication at Scale
Emergent communication aims for a better understanding of human language evolution and building more efficient representations.
Meta Learning for Multi-agent Communication
Recent works have shown remarkable progress in training artificial agents to understand natural language but are focused on using large amounts of raw data involving huge compute requirements.
Mind the Gap: Assessing Temporal Generalization in Neural Language Models
Hence, given the compilation of ever-larger language modelling datasets, combined with the growing list of language-model-based NLP applications that require up-to-date factual knowledge about the world, we argue that now is the right time to rethink the static way in which we currently train and evaluate our language models, and develop adaptive language models that can remain up-to-date with respect to our ever-changing and non-stationary world.
Emergent Communication under Competition
First, we show that communication is proportional to cooperation, and it can occur for partially competitive scenarios using standard learning algorithms.
Negotiating Team Formation Using Deep Reinforcement Learning
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals.
Emergent Multi-Agent Communication in the Deep Learning Era
The ability to cooperate through language is a defining feature of humans.
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning
We present a method for combining multi-agent communication and traditional data-driven approaches to natural language learning, with an end goal of teaching agents to communicate with humans in natural language.
Experience Grounds Language
Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates.
Shaping representations through communication: community size effect in artificial learning systems
Motivated by theories of language and communication that explain why communities with large numbers of speakers have, on average, simpler languages with more regularity, we cast the representation learning problem in terms of learning to communicate.
Biases for Emergent Communication in Multi-agent Reinforcement Learning
We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL
Therefore, we also employ influence to train agents to use an explicit communication channel, and find that it leads to more effective communication and higher collective reward.
Learning and Evaluating General Linguistic Intelligence
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly.
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks.
Emergent Communication through Negotiation
We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.
Compositional Obverter Communication Learning From Raw Visual Input
Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e. g. hand- engineered features).
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL).
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017).
CommAI: Evaluating the first steps towards a useful general AI
With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal.
Multi-Agent Cooperation and the Emergence of (Natural) Language
The sender is told one of them is the target and is allowed to send a message from a fixed, arbitrary vocabulary to the receiver.
The LAMBADA dataset: Word prediction requiring a broad discourse context
We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task.
Towards Multi-Agent Communication-Based Language Learning
We propose an interactive multimodal framework for language learning.
The red one!: On learning to refer to things based on their discriminative properties
As a first step towards agents learning to communicate about their visual environment, we propose a system that, given visual representations of a referent (cat) and a context (sofa), identifies their discriminative attributes, i. e., properties that distinguish them (has_tail).
Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation
We introduce language-driven image generation, the task of generating an image visualizing the semantic contents of a word embedding, e. g., given the word embedding of grasshopper, we generate a natural image of a grasshopper.
From Visual Attributes to Adjectives through Decompositional Distributional Semantics
By building on the recent "zero-shot learning" approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available.
Combining Language and Vision with a Multimodal Skip-gram Model
We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account.
Improving zero-shot learning by mitigating the hubness problem
The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels.
Cross-Language Authorship Attribution
We propose a number of cross-language stylometric features for the task of CLAA, such as those based on sentiment and emotional markers.
