Latest Research
Sinkhorn Divergence of Topological Signature Estimates for Time Series Classification
Distinguishing between classes of time series sampled from dynamic systems is a common challenge in systems and control engineering, for example in the context of health monitoring, fault detection, and quality control.
Learn a Prior for RHEA for Better Online Planning
In this paper, we propose to learn a prior for RHEA in an offline manner by training a value network and a policy network.
Language Models are Unsupervised Multitask Learners
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
COMMON SENSE REASONING DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts.
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
• dnguyengithub/AudioNovelty
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In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection.
3D Face Modeling from Diverse Raw Scan Data
Traditional 3D models learn a latent representation of faces using linear subspaces from no more than 300 training scans of a single database.
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps
Saliency Map, the gradient of the score function with respect to the input, is the most basic technique for interpreting deep neural network decisions.
SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
From our extensive evaluation of 20 architectures, we report a highest score of 71.6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR LSTM model with bloom filter embeddings and bidirectional segmentation.
Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications
We show that these primitives can then be used to implement flexible sharing policies such as fairness, prioritization, and packing for various use cases.
Contrastive Variational Autoencoder Enhances Salient Features
The cVAE explicitly models latent features that are shared between the datasets, as well as those that are enriched in one dataset relative to the other, which allows the algorithm to isolate and enhance the salient latent features.