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Common Sense Reasoning

25 papers with code · Reasoning

Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.

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Greatest papers with code

A Simple Method for Commonsense Reasoning

7 Jun 2018tensorflow/models

For example, it is difficult to use neural networks to tackle the Winograd Schema dataset~\cite{levesque2011winograd}. In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning.

COMMON SENSE REASONING

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

11 Oct 2018google-research/bert

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers.

COMMON SENSE REASONING CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION QUESTION ANSWERING

Language Models are Unsupervised Multitask Learners

Preprint 2019 openai/gpt-2

Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText.

COMMON SENSE REASONING DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION

A Neural Conversational Model

19 Jun 2015farizrahman4u/seq2seq

We find that this straightforward model can generate simple conversations given a large conversational training dataset. On a noisy open-domain movie transcript dataset, the model can perform simple forms of common sense reasoning.

COMMON SENSE REASONING

Temporal Relational Reasoning in Videos

ECCV 2018 metalbubble/TRN-pytorch

Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales.

ACTIVITY RECOGNITION COMMON SENSE REASONING HUMAN-OBJECT INTERACTION DETECTION RELATIONAL REASONING

Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

22 Jul 2016braincorp/PVM

These regularities are hard to label for training supervised machine learning algorithms; consequently, algorithms need to learn these regularities from the real world in an unsupervised way. The results suggest a new class of AI algorithms that uniquely combine prediction and scalability in a way that makes them suitable for learning from and --- and eventually acting within --- the real world.

COMMON SENSE REASONING VISUAL TRACKING

DKN: Deep Knowledge-Aware Network for News Recommendation

25 Jan 2018hwwang55/DKN

To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. We also validate the efficacy of the usage of knowledge in DKN.

CLICK-THROUGH RATE PREDICTION COMMON SENSE REASONING RECOMMENDATION SYSTEMS

Modeling User Exposure in Recommendation

23 Oct 2015dawenl/expo-mf

The exposure is modeled as a latent variable and the model infers its value from data. In doing so, we recover one of the most successful state-of-the-art approaches as a special case of our model, and provide a plug-in method for conditioning exposure on various forms of exposure covariates (e.g., topics in text, venue locations).

COLLABORATIVE FILTERING COMMON SENSE REASONING

The "something something" video database for learning and evaluating visual common sense

ICCV 2017 TwentyBN/smth-smth-v2-baseline-with-models

Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification. One obstacle that prevents networks from reasoning more deeply about complex scenes and situations, and from integrating visual knowledge with natural language, like humans do, is their lack of common sense knowledge about the physical world.

COMMON SENSE REASONING OBJECT CLASSIFICATION VIDEO PREDICTION

Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions

ICLR 2018 sjoerdvansteenkiste/Relational-NEM

Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved.

COMMON SENSE REASONING