Search results

This team, consisting of PhD and Masters students from Carnegie Mellon University, brings together experience in chatbot interaction strategies, question answering, neural modeling and machine learning.

We are the Alquist team from CTU, Prague, Czech Republic.

We are Edina, from the University of Edinburgh, a world-leading institution in Artificial Intelligence.

Our international team of 6 PhD students and faculty advisors has a wide range of experience from both academic and industrial research.

We are five graduate and undergraduate students of cognitive science, computer science, and applied physics from Rensselaer Polytechnic Institute.

Our team has been developed from a deep learning study group at SNU.

The DeisBot team is comprised of seven graduate students in the Computational Linguistics department at Brandeis University.

Online optimization is central to display advertising, where we must sequentially allocate ad impressions to maximize the total welfare among advertisers, while respecting various advertiser-specified long-term constraints (e.g., total amount of the ad’s budget that is consumed at the end of the campaign). In this paper, we present the online dual decomposition (ODD) framework for large-scale, online, distributed

We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on several orders of magnitude larger problems than previous related work. In a study on

We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smoothing based evaluation approach is employed to measure keyword spotting performance. Our experimental

We propose improved Deep Neural Network (DNN) training loss functions for more accurate single keyword spotting on resource-constrained embedded devices. The loss function modifications consist of a combination of multi-task training and weighted cross entropy. In the multi-task architecture, the keyword DNN acoustic model is trained with two tasks in parallel - the main task of predicting the keyword-specific

Voice search applications are typically evaluated by comparing the predicted query to a reference human transcript, regardless of the search results returned by the query. While we find that an exact transcript match is highly indicative of user satisfaction, a transcript which does not match the reference still produces satisfactory search results a significant fraction of the time. This paper therefore

Structured prediction from bandit feedback describes a learning scenario where instead of having access to a gold standard structure, a learner only receives partial feedback in form of the loss value of a predicted structure. We present new learning objectives and algorithms for this interactive scenario, focusing on convergence speed and ease of elicitability of feedback. We present supervised-to-bandit

We propose a technique that combines generative adversarial networks with probabilistic graphical models to explicitly model dependencies in structured distributions. Generative adversarial structured networks (GASNs) produce samples by passing random inputs through a neural network to construct the potentials of a graphical model; maximum a-posteriori inference in this graphical model then yields a sample

Inverse Propensity Score estimator (IPS) is a basic, unbiased, offpolicy evaluation technique to measure the impact of a user-interactive system without serving live traffic. We present our work on applying IPS to real-world settings by addressing some practical challenges, thereby enabling successful policy evaluation. In particular, we show that off-policy evaluation can be impossible in the absence of

In this paper we focus on the anomaly detection problem for dynamic data streams through the lens of random cut forests. We investigate a robust random cut data structure that can be used as a sketch or synopsis of the input stream. We provide a plausible definition of non-parametric anomalies based on the influence of an unseen point on the remainder of the data, i.e., the externality imposed by that point

In structured prediction, a predictor optimizes an objective function over a combinatorial search space, such as the set of all image segmentations, or the set of all part-of-speech taggings. Unfortunately, finding the optimal structured labeling—sometimes referred to as maximum a posteriori (MAP) inference—is, in general, NP-hard [12], due to the combinatorial structure of the problem. Many inference approximations

In this paper we consider two special cases of the “cover-by-pairs” optimization problem that arise when we need to place facilities so that each customer is served by two facilities that reach it by disjoint shortest paths. These problems arise in a network traffic monitoring scheme proposed by Breslau et al. and have potential applications to content distribution. The “set-disjoint” variant applies to

Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired. Visual browsing systems allow e-commerce platforms to address these scenarios while offering the user an engaging shopping experience. Here we explore extensions in the direction of adaptive personalization and item diversification

Amazon is one of the world’s largest e-commerce sites and Amazon Search powers the majority of Amazon’s sales. As a consequence, even small improvements in relevance ranking both positively influence the shopping experience of millions of customers and significantly impact revenue. In the past, Amazon’s product search engine consisted of several handtuned ranking functions using a handful of input features