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Piero Molino

Piero Molino
8 BLOG ARTICLES 8 RESEARCH PAPERS
Piero is a Staff Research Scientist in the Hazy research group at Stanford University. He is a former founding member of Uber AI where he created Ludwig, worked on applied projects (COTA, Graph Learning for Uber Eats, Uber’s Dialogue System) and published research on NLP, Dialogue, Visualization, Graph Learning, Reinforcement Learning and Computer Vision.

Engineering Blog Articles

Ludwig v0.3 Introduces Hyperparameter Optimization, Transformers and TensorFlow 2 support

In February 2019, Uber released Ludwig, an open source, code-free deep learning (DL) toolbox that gives non-programmers and advanced machine learning (ML) practitioners alike...

Meta-Graph: Few-Shot Link Prediction Using Meta-Learning

Uber AI introduces Meta-Graph, a new few-shot link prediction framework that facilitates the more accurate training of ML models that quickly adapt to new graph data.

Controlling Text Generation with Plug and Play Language Models

Plug and Play Language Model, introduced by Uber AI Labs, gives NLP practitioners the flexibility to plug in one or more simple attribute models into a large, unconditional language model.

Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations

By integrating graph learning techniques with our Uber Eats recommendation system, we created a more seamless and individualized user experience for eaters on our platform.

Ludwig v0.2 Adds New Features and Other Improvements to its Deep Learning Toolbox

Ludwig version 0.2 integrates with Comet.ml, adds a new serving functionality, and incorporates the BERT text encoder, among other new features.

Introducing Ludwig, a Code-Free Deep Learning Toolbox

Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

As powerful and widespread as convolutional neural networks are in deep learning, AI Labs’ latest research reveals both an underappreciated failing and a simple fix.

COTA: Improving Uber Customer Care with NLP & Machine Learning

In this article, Uber Engineering introduces our Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.

Research Papers

Plug and Play Language Models: A Simple Approach to Controlled Text Generation

S. Dathathri, A. Madotto, J. Lan, J. Hung, E. Frank, P. Molino, J. Yosinski, R. Liu
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. [PDF]
International Conference on Learning Representations (ICLR), 2020

Flexibly-Structured Model for Task-Oriented Dialogues

L. Shu, P. Molino, M. Namazifar, H. Xu, B. Liu, H. Zheng, G. Tur
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, 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. The policy engine and language generation tasks are modeled jointly following that. [...] [PDF]
2019

Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning

A. Papangelis, Y.-C. Wang, P. Molino, G. Tur
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 language understanding (NLU) and generation (NLG) networks for each agent and let the agents interact online. [...] [PDF]
Special Interest Group on Discourse and Dialogue (SIGDIAL), 2019

Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models

J. Zhang, Y. Wang, P. Molino, L. Li, D. Ebert
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 utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. [...] [PDF]
IEEE Visualization (IEEE VIS), 2018

COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks

P. Molino, H. Zheng, Y.-C. 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 and reliability of customer support for end users through automated ticket classification and answers selection for support representatives. [...] [PDF]
ACM SIGKDD International Conference on Knowledge Discovery and Data Science (KDD), 2018

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

R. Liu, J. Lehman, P. Molino, F.i Such, E. Frank, A. Sergeev, J. Yosinski
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 neural networks may be appropriate. 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. [...] [PDF]
Advances in Neural Information Processing Systems (NeurIPS), 2018

Incorporating the Structure of the Belief State in End-to-End Task-Oriented Dialogue Systems

L. Shu, P. Molino, M. Namazifar, B. Liu, H. Xu, H. Zheng, and G. Tur
End-to-end trainable networks try to overcome error propagation, lack of generalization and overall brittleness of traditional modularized task-oriented dialogue system architectures. Most proposed models expand on the sequence-to-sequence architecture. Some of them don’t track belief state, which makes it difficult to interact with ever-changing knowledge bases, while the ones that explicitly track the belief state do it with classifiers. The use of classifiers suffers from the out-of-vocabulary words problem, making these models hard to use in real-world applications with ever-changing knowledge bases. We propose Structured Belief Copy Network (SBCN), a novel end-to-end trainable architecture that allows for interaction with external symbolic knowledge bases and solves the out-of-vocabulary problem at the same time. [...] [PDF]
Conversational Intelligence Challenge at Conference on Neural Information Processing Systems (ConvAI @ NeurIPS), 2018

Characterizing how Visual Question Answering models scale with the world

E. Bingham, P. Molino, P. Szerlip, F. Obermeyer, N. Goodman
Detecting differences in generalization ability between models for visual question answering tasks has proven to be surprisingly difficult. We propose a new statistic, asymptotic sample complexity, for model comparison, and construct a synthetic data distribution to compare a strong baseline CNN-LSTM model to a structured neural network with powerful inductive biases. [...] [PDF]
ViGIL @ NeurIPS(NeurIPS), 2017

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