Efficient Learning with Label-Dependent Weight Functions


Efficient Learning with Label-Dependent Weight Functions – We present the first ever dataset of the full word labels in the context of machine learning (ML) classification. By modeling the label distribution under the full word label distribution, we propose a novel and practical learning algorithm that combines Bayesian and Bayesian methods. We show the advantage of our algorithm, using the new training dataset as our dataset, and also show that the new dataset will provide a valuable framework for analyzing and designing a Bayesian ML methodology. The dataset is composed of 5,700 annotated sentences from 2,000+ annotated datasets. For each annotation, we propose a label-dependent weight function, and test it on various datasets, while incorporating a data-driven approach for learning. The experimental results show that the proposed approach achieves state-of-the-art performance when evaluated by using the same label distribution (without using any label labels). We also present experiments showing that the proposed method generalizes well to a variety of ML tasks, including learning to classify trees and the estimation of word embeddings.

We present a framework for predicting the trajectory of a moving object from its point of interest and then inferring whether the object moved or not. Unlike other existing methods relying on hand-crafted and learned features, our approach relies on the model-structured representations of the objects’ motion trajectories. To efficiently learn the model-structured representations, we propose a neural network based on convolutional neural networks (CNN). We first use a set of trajectories in a model to model the trajectory. Each trajectory is estimated with a convolutional neural network, which is fed a set of hand-crafted features. Then, a trajectory model is constructed that exploits those trajectories to infer which trajectories are associated with the object, and which trajectories are associated with the trajectory. Based on this learning, we propose two learning algorithms and their optimization strategies. We also propose an online learning procedure to automatically update trajectories and achieve better target locations. The algorithm is evaluated on both standard and synthetic datasets.

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Efficient Learning with Label-Dependent Weight Functions

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  • On the Generalizability of the Population Genetics Dataset

    Bidirectional Multiple Attractor Learning for Multi-Target Tracking and TrackingWe present a framework for predicting the trajectory of a moving object from its point of interest and then inferring whether the object moved or not. Unlike other existing methods relying on hand-crafted and learned features, our approach relies on the model-structured representations of the objects’ motion trajectories. To efficiently learn the model-structured representations, we propose a neural network based on convolutional neural networks (CNN). We first use a set of trajectories in a model to model the trajectory. Each trajectory is estimated with a convolutional neural network, which is fed a set of hand-crafted features. Then, a trajectory model is constructed that exploits those trajectories to infer which trajectories are associated with the object, and which trajectories are associated with the trajectory. Based on this learning, we propose two learning algorithms and their optimization strategies. We also propose an online learning procedure to automatically update trajectories and achieve better target locations. The algorithm is evaluated on both standard and synthetic datasets.


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