225 citations · 631 across the 14 of their papers we have counts for
18 papers
PolyAlign: Conditional Human-Distribution Alignment
L. D. M. S. Sai Teja, Ufaq Khan, Sathira Silva +2
Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assistant behavior. While effective fo…
Witgenstein's influence on artificial intelligence
Piero Molino, Jacopo Tagliabue
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 s…
Personalized Benchmarking with the Ludwig Benchmarking Toolkit
Avanika Narayan, Piero Molino, Karan Goel +2
The rapid proliferation of machine learning models across domains and deployment settings has given rise to various communities (e.g. industry practitioners) which seek to benchmar…
Declarative Machine Learning Systems
Piero Molino, Christopher Ré
In the last years machine learning (ML) has moved from a academic endeavor to a pervasive technology adopted in almost every aspect of computing. ML-powered products are now embedd…
Controllable Text Generation with Focused Variation
Lei Shu, Alexandros Papangelis, Yi-Chia Wang +5
This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from t…
Joint Contextual Modeling for ASR Correction and Language Understanding
Yue Weng, Sai Sumanth Miryala, Chandra Khatri +8
The quality of automatic speech recognition (ASR) is critical to Dialogue Systems as ASR errors propagate to and directly impact downstream tasks such as language understanding (LU…
Plato Dialogue System: A Flexible Conversational AI Research Platform
Alexandros Papangelis, Mahdi Namazifar, Chandra Khatri +3
As the field of Spoken Dialogue Systems and Conversational AI grows, so does the need for tools and environments that abstract away implementation details in order to expedite the…
Exploration Based Language Learning for Text-Based Games
Andrea Madotto, Mahdi Namazifar, Joost Huizinga +7
This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describ…
Meta-Graph: Few Shot Link Prediction via Meta Learning
Avishek Joey Bose, Ankit Jain, Piero Molino +1
We consider the task of few shot link prediction on graphs. The goal is to learn from a distribution over graphs so that a model is able to quickly infer missing edges in a new gra…
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Sumanth Dathathri, Andrea Madotto, Janice Lan +5
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated languag…
Ludwig: a type-based declarative deep learning toolbox
Piero Molino, Yaroslav Dudin, Sai Sumanth Miryala
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 writi…
Modeling Multi-Action Policy for Task-Oriented Dialogues
Lei Shu, Hu Xu, Bing Liu +1
Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only o…
Flexibly-Structured Model for Task-Oriented Dialogues
Lei Shu, Piero Molino, Mahdi Namazifar +4
This paper proposes a novel end-to-end architecture for task-oriented dialogue systems. It is based on a simple and practical yet very effective sequence-to-sequence approach, wher…
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
Alexandros Papangelis, Yi-Chia Wang, Piero Molino +1
We present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language. Using DSTC2 as seed data, we trained natural…
Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Piero Molino, Yang Wang, Jiawei Zhang
Embeddings are a fundamental component of many modern machine learning and natural language processing models. Understanding them and visualizing them is essential for gathering in…
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models
Jiawei Zhang, Yang Wang, Piero Molino +2
Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that uti…
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
Piero Molino, Huaixiu Zheng, Yi-Chia Wang
For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed…
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Rosanne Liu, Joel Lehman, Piero Molino +4
Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional ne…