Augmenting a neural network with memory that can grow without growing the number of trained parameters is a recent powerful concept with many exciting applications. We propose a design of memory augmented neural networks (MANNs) called Labeled Memory Networks (LMNs) suited for tasks requiring online adaptation in classification models. LMNs organize the memory with classes as the primary key.The memory acts as a second boosted stage following a regular neural network thereby allowing the memory and the primary network to play complementary roles. Unlike existing MANNs that write to memory for every instance and use LRU based memory replacement, LMNs write only for instances with non-zero loss and use label-based memory replacement. We demonstrate significant accuracy gains on various tasks including word-modelling and few-shot learning. In this paper, we establish their potential in online adapting a batch trained neural network to domain-relevant labeled data at deployment time. We show that LMNs are better than other MANNs designed for meta-learning. We also found them to be more accurate and faster than state-of-the-art methods of retuning model parameters for adapting to domain-specific labeled data.

I love it when I get feedback from a blog that I've written. I appreciate the different perspectives and insights that others bring to a topic of interest. And no blog that I've written has drawn more comments than my blog, "Isaac Asimov: The 4th Law of Robotics." The section of the blog that fueled the most comments stem from a scene in the movie I, Robot where Detective Spooner (played by Will Smith) is explaining to Doctor Calvin (who is responsible for giving robots human-like behaviors) why he distrusts and hates robots. He is describing an incident where his police car crashed into another car and both cars were thrown into a cold and deep river – certain death for all occupants.

Why watch video on a phone or a tablet when you can get a device for as little as $30 to stream shows on a big-screen TV? Apple, Google, Amazon, Roku and dozens of others are all competing to be your gateway to online video. Which device you need will largely depend on what services you watch and what kind of TV you have. The days when watching TV was a choice between a handful of channels are long gone. Now, there are a dizzying array of streaming devices on the market, with all of the big manufacturers battling for control of the living room - so how do you choose what to buy? Of course, the device alone won't be enough.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. It's that time of year again where everyone makes a list--some check it twice--of the gifts they want for the holiday season. Amazon is no different with its wish list function. The website keeps track of the top items people are adding to their wish lists heading into holiday season, and they display them on their "Most Wished For" page.

"Coditany of Timeness" is a convincing lo-fi black metal album, complete with atmospheric interludes, tremolo guitar, frantic blast beats and screeching vocals. But the record, which you can listen to on Bandcamp, wasn't created by musicians. Instead, it was generated by two musical technologists using a deep learning software that ingests a musical album, processes it, and spits out an imitation of its style. To create Coditany, the software broke "Diotima," a 2011 album by a New York black metal band called Krallice, into small segments of audio. Then they fed each segment through a neural network -- a type of artificial intelligence modeled loosely on a biological brain -- and asked it to guess what the waveform of the next individual sample of audio would be.

I'm not read up on speech processing (I focus on music) but that sounds really cool and useful (e.g. There seems to be a bunch of stuff on the topic. Anyway, if you have a dataset of speech audio files and corresponding labels with at what times stutters occur, then this would be a straight forward problem for a HMM or RNN and you might not benefit from a separate dimensionality reduction step. As the classification problem is binary (I'm assuming a spectral frame is fluent or not) it might be enough to do a standard STFT, turn the labels into a binary vector of the same time resolution, and train a baseline LSTM in Keras. I'd see how far that gets before looking into trickier stuff like filterbanks and CTC.

Telltale's original Walking Dead game was special, blending a gut-wrenching storyline with interesting, believable characters. Five years and two seasons later (four if you count 400 Days and Michonne) the adventure has started to show its age. So for The Walking Dead Collection -- a new bundle that launches on December 5th -- the developer has given everything a visual upgrade. To explain the changes, Telltale has released a video comparing the two versions during a pivotal scene -- Lee and Clementine's first meeting. At first, the differences might seem small.

A subreddit dedicated for learning machine learning. Feel free to share any educational resources of machine learning. Also, we are a beginner-friendly subreddit, so don't be afraid to ask questions! This can include questions that are non-technical, but still highly relevant to learning machine learning such as a systematic approach to a machine learning problem.

Smart speakers are set to be the hottest Christmas gift this year. On Black Friday, Amazon dropped the price of its core Echo product to £79 (it is back up to £90 now), while Google slashed the cost of its Home device from £129 to £77.50 at most outlets (it is also back up now). Meanwhile, Apple is promising to launch its version, HomePod, although the price point is rumoured to be significantly higher. With the pre-Christmas launch of the Echo Show, which ups Alexa's game with a built-in screen, are they the next must-have device? A simple voice command can fill your room with music – and change tunes whenever you wish. They will answer questions on a vast range of topics, set alarms, tell you the weather and what your commute holds in store.

Melvin Wevers, a researcher in the Digital Humanities Group of the KNAW Humanities Cluster, created --a tool that analyses adverts in historical newspapers-- together with Juliette Lonij, as a Researcher-in-Residence at the Royal Library of the Netherlands. Clemens Neudecker, a researcher at the Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, is working on making Europeana Newspapers --a huge set of historical newspapers-- available through Europeana Collections. Here, they tell us more about how their areas of expertise come together.... Tell us about SIAMESE - the tool you've developed to analyse images in newspapers. Melvin: SIAMESE uses machine-learning techniques (like Convolutional Neural Networks and Approximate Nearest Neighbour algorithms) to detect shapes and objects, and search for similar images. For instance, it can recognise visual elements representing a car in an advert, and come up with similar car ads.