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Found 18 papers, 11 papers with code
Witgenstein's influence on artificial intelligence
We examine how much of the contemporary progress in artificial intelligence (and, specifically, in natural language processing), can be, more or less directly, traced back to the seminal work and ideas of the Austrian-British philosopher Ludwig Wittgenstein, with particular focus on his late views.
Personalized Benchmarking with the Ludwig Benchmarking Toolkit
LBT provides a configurable interface for controlling training and customizing evaluation, a standardized training framework for eliminating confounding variables, and support for multi-objective evaluation.
Declarative Machine Learning Systems
In this article we will describe how ML systems are currently structured, highlight important factors for their success and adoption, what are the issues current ML systems are facing and how the systems we developed addressed them.
Controllable Text Generation with Focused Variation
This work introduces Focused-Variation Network (FVN), a novel model to control language generation.
Joint Contextual Modeling for ASR Correction and Language Understanding
As a baseline approach, we trained task-specific Statistical Language Models (SLM) and fine-tuned state-of-the-art Generalized Pre-training (GPT) Language Model to re-rank the n-best ASR hypotheses, followed by a model to identify the dialog act and slots.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exploration Based Language Learning for Text-Based Games
In this work, we propose to use the exploration approach of Go-Explore for solving text-based games.
Plato Dialogue System: A Flexible Conversational AI Research Platform
Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
Meta-Graph: Few Shot Link Prediction via Meta Learning
We consider the task of few shot link prediction on graphs.
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.
Ludwig: a type-based declarative deep learning toolbox
In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code.
Modeling Multi-Action Policy for Task-Oriented Dialogues
Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system.
Flexibly-Structured Model for Task-Oriented Dialogues
It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot.
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
and their own objectives, and can only interact via natural language they generate.
Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Embeddings are a fundamental component of many modern machine learning and natural language processing models.
Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Embeddings are a fundamental component of many modern machine learning and natural language processing models.
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models
We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner.
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate transform problem, which simply requires learning a mapping between coordinates in (x, y) Cartesian space and one-hot pixel space.
Ranked #863 on
Image Classification
on ImageNet
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues.
