Skip to search formSkip to main contentSkip to account menu

Boosting the margin: A new explanation for the effectiveness of voting methods

It is shown that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error.
View via Publisher (opens in a new tab)

SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces

This work has developed a new point-based POMDP algorithm that exploits the notion of optimally reachable belief spaces to improve com- putational efficiency and substantially outperformed one of the fastest existing point- based algorithms.
View via Publisher (opens in a new tab)

Building text classifiers using positive and unlabeled examples

A more principled approach to solving the problem of building text classifiers using positive and unlabeled examples based on a biased formulation of SVM is proposed, and it is shown experimentally that it is more accurate than the existing techniques.
View on IEEE (opens in a new tab)

Question classification using support vector machines

This paper proposes to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions, and describes how the tree Kernel can be computed efficiently by dynamic programming.
View on ACM (opens in a new tab)

An Unsupervised Neural Attention Model for Aspect Extraction

A novel neural approach that improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings, and uses an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects.
View on ACL (opens in a new tab)

Partially Supervised Classification of Text Documents

This paper shows that the problem of identifying documents from a set of documents of a particular topic or class P and a large set M of mixed documents, and that under appropriate conditions, solutions to the constrained optimization problem will give good solution to the partially supervised classification problem.

DESPOT: Online POMDP Planning with Regularization

This paper presents an online POMDP algorithm that alleviates these difficulties by focusing the search on a set of randomly sampled scenarios, and gives an output-sensitive performance bound for all policies derived from a DESPOT, and shows that R-DESPOT works well if a small optimal policy exists.
View PDF on arXiv (opens in a new tab)

Convolutional Sequence to Sequence Model for Human Dynamics

This work presents a novel approach to human motion modeling based on convolutional neural networks (CNN), which is able to capture both invariant and dynamic information of human motion, which results in more accurate predictions.
View on IEEE (opens in a new tab)

An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis

An interactive multi-task learning network (IMN) is proposed which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level and introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables.
View on ACL (opens in a new tab)

Planning under Uncertainty for Robotic Tasks with Mixed Observability

A factored model is used to represent separately the fully and partially observable components of a robot’s state and derive a compact lower-dimensional representation of its belief space that can be combined with any point-based algorithm to compute approximate POMDP solutions.
View on SAGE (opens in a new tab)
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)