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...length k, called k-means, and then piecing them together or aligning them to a reference genome. Next-generation sequencing is relatively fast and inexp ...s and small indels are technically challenging since it requires a program to distinguish between genuinely novel mutations and errors in the sequencing ...

...toluie (Paper: "Visualizing Similarity Data with a Mixture of Maps") (Link to paper: [http://www.cs.toronto.edu/~hinton/absps/ampaper.pdf ]) ...

...y of 87%, while the actual test accuracy is only 72% in (Guo et al., 17'). To address this issue, more and more researchers work on improving the '''cali ...ms fix this by adjusting the output of a well-trained model and dates back to the classical methods of Platt scaling (Platt et al., 1999), histogram binn ...

...o separate entities is key to understanding what is around us and it helps to reason about the behavior of objects in the scene. ...med "object detection". There are four distinct levels of detection (refer to Figure 1 for a visual cue): ...

...trol signals for manipulating tone, timbre, and dynamics during playback. To train such a data expensive model the authors highlight the need for a larg Proposed Wavenet-style autoencoder that learn to encode temural data over a long term audio structures without requiring ext ...

...thod takes advantage of unbalanced label distributions by forming clusters to reduce training time. The authors experimented on five different datasets a ...ining the Probabilistic Label Tree [5] method and the Adaptive Softmax [6] to propose APLC. ...

Stereo-vision has been used extensively for mobile robots in identifying near-to-far obstacles in its path, but is limited by it's max range of 12 meters. F ...ers. This approach has been implemented and tested on the Learning Applied to Ground Robots (LAGR) provided by the National Robotics Engineering Center ( ...

...to the ability of a model to also assign a confidence score for its answer to the question. ...l reader since neural reading comprehension is expensive. It's impractical to process billions of documents using a neural reader. ...

...content of a signal lays in a few samples with large magnitude. This lead to study and investigation on a class of signals, known as compressible signal ...an we sample compressible signals in a compressed way? Is there any method to sense only those large value coefficients? In parallel works by Donoho <ref ...

...olve a wide of range problems. We mainly introduce XGBoost, a scalable end-to-end tree boosting system in this page. We demonstrate the exact greedy algo So the target function that needed to optimize is:<math>\sum_{i=1}^n l(y_i,\hat y_i)+\sum^K_{k=1}\omega(f_k), f_k ...

...the global semantic, the probability of each learned latent topic is used to learn the local structure of a word sequence. ...on of a document to predict the topic distribution of the document in wish to identify the global semantic meaning of documents. ...

...escribes a method to learn a nonlinear transformation from the input space to a low-dimensional ...sed dimensions for nearest neighbor classifications are used by the method to explicitly represent ...

...d look like. This work explores whether a similar principle can be applied to teaching machines: can we supervise networks without individual examples by ...thout labels by learning from constraints that are known to hold according to prior domain knowledge. By training without direct examples of the values o ...

...pular online shopping website Amazon.com for recommending related products to users of Amazon.com based on what these users have recently purchased from Our goal, then, is to predict or infer the other preferences---in a sense, completing the matrix. ...

...Adversarial attacks are instances where people intentionally design inputs to cause misclassification in the model. Copyright detection systems are vulnerable to attacks for three reasons: ...

...ms. In such situations, researchers are faced with the challenge of trying to generate results based on partial or incomplete datasets. Regularisation te ...as a regularisation agent, constraining the space of acceptable solutions to help the optimisation converge more quickly and more accurately. ...

Recognizing the category of the dominant object in an image is a task to which Convolutional Networks (ConvNets) have been applied for many years. C 1. The first idea in addressing this is to apply a ConvNet at multiple locations in the image, in a sliding window fas ...

...amount of work to learn more than one language past childhood. The ability to efficiently and quickly translate between languages would then be of great ...s that capture their meaning, as sentences with similar meanings are close to each other while sentences with different meanings will be far. ...

...amount of work to learn more than one language past childhood. The ability to efficiently and quickly translate between languages would then be of great ...s that capture their meaning, as sentences with similar meanings are close to each other while sentences with different meanings will be far. ...

...amount of work to learn more than one language past childhood. The ability to efficiently and quickly translate between languages would then be of great ...s that capture their meaning, as sentences with similar meanings are close to each other while sentences with different meanings will be far. ...