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web framework java kotlin
6 days ago
Resilience4j User Guide
hysterix circuit breaker java
6 days ago
Java Automatic Term Extraction toolkit
a library of state-of-the-art term extraction algorithms and framework for developing term extraction algorithms
7 days ago
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
We present a tutorial on Bayesian optimization, a method of finding
the maximum of expensive cost functions. Bayesian optimization employs
the Bayesian technique of setting a prior over the objective function and
combining it with evidence to get a posterior function. This permits a
utility-based selection of the next observation to make on the objective
function, which must take into account both exploration (sampling from
areas of high uncertainty) and exploitation (sampling areas likely to offer
improvement over the current best observation). We also present two
detailed extensions of Bayesian optimization, with experiments—active
user modelling with preferences, and hierarchical reinforcement learning—
and a discussion of the pros and cons of Bayesian optimization based on
our experiences
bayesian  optimization 
8 days ago
How to cache HTTP range requests - Moz
collapsed forwarding
CARP support
range request caching
varnish  http  cache 
9 days ago
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions [pdf]
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are even more accurate while maintaining 8x faster test time speeds.
NER  papers 
10 days ago
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