Search Results for author:
Found 14 papers, 3 papers with code
Gamified crowd-sourcing of high-quality data for visual fine-tuning
This paper introduces Gamified Adversarial Prompting (GAP), a framework that crowd-sources high-quality data for visual instruction tuning of large multimodal models.
Ranked #22 on
Visual Question Answering
on MM-Vet
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
What we need to learn if we want to do and not just talk
In task-oriented dialog, agents need to generate both fluent natural language responses and correct external actions like database queries and updates.
Achieving Fluency and Coherency in Task-oriented Dialog
We show how to combine nearest neighbor and Seq2Seq methods in a hybrid model, where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialog coherency and generate accurate external actions.
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient
Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL).
Visualizing the Consequences of Evidence in Bayesian Networks
This paper addresses the challenge of viewing and navigating Bayesian networks as their structural size and complexity grow.
Predicting Surgery Duration with Neural Heteroscedastic Regression
Scheduling surgeries is a challenging task due to the fundamental uncertainty of the clinical environment, as well as the risks and costs associated with under- and over-booking.
Learning to Diagnose with LSTM Recurrent Neural Networks
We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements.
A Critical Review of Recurrent Neural Networks for Sequence Learning
Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes.
Efficient Elastic Net Regularization for Sparse Linear Models
This paper provides closed-form updates for the popular squared norm $\ell^2_2$ and elastic net regularizers.
Differential Privacy and Machine Learning: a Survey and Review
The objective of machine learning is to extract useful information from data, while privacy is preserved by concealing information.
Thresholding Classifiers to Maximize F1 Score
As another special case, if the classifier is completely uninformative, then the optimal behavior is to classify all examples as positive.
Learning the k in k-means
The G-means algorithm is based on a statistical test for the hypothesis that a subset of data follows a Gaussian distribution.
