Recent state-of-the-art architectures have employed a number of additional components to complement the convolution operation. In this post, I would be explaining some of the most important components that have improved both the speed and accuracy of modern convolutional neural networks. I would begin by explaining the theory of each of the components and finalize with a practical implementation in keras. The first secret sauce that has made CNNs very effective is pooling. Pooling is a vector to scalar transformation that operates on each local region of an image, just like convolutions do, however, unlike convolutions, they do not have filters and do not compute dot products with the local region, instead they compute the average of the pixels in the region (Average Pooling) or simply picks the pixel with the highest intensity and discards the rest (Max Pooling).

Google's artificial intelligence (AI) division DeepMind is developing a system that could one day predict when a hospital patient is at risk of dying, even if serious signs of illness are not immediately apparent. With the assistance of the US Veterans Administration, the partnership is seeking to understand the changes in a hospital patient's condition that could result in death if left unchecked by a doctor or nurse, Alphr reports. To do this, the website says, the partnership has fed 700,000 medical records to an AI programme to identify signs of "human error" in treatment. The records are from US army and police veterans. The partnership's first priority is to use AI to understand acute kidney injury, says MedCityNews, which is "a complication related to patient deterioration".

I'm sure you'll agree that Artificial Intelligence, in particular Deep Learning, has made huge strides in the last 5 years or so. But what began as a relatively niche field with just a handful of researchers, has now become so mainstream that the apps and services that we use everyday now use Deep Learning to perform tasks that were unthinkable not that long ago. It's been around since the 1940s when Warren McCulloch and Walter Pitts created a computational model for neural networks based on mathematics and algorithms. However "Deep Learning" only began to gain in popularity in the mid-2000s when Geoffrey Hinton and Ruslan Salakhutdinov released a paper showed how a multi-layered neural network could be pre-trained one layer at a time. In 2009 it was discovered that with large enough datasets, you didn't actually need the pre-training and that error rates could drop significantly as a result.

Mujoco is mostly a physics engine, and I'm willing to bet that whatever parts you're thinking of when you say it's "more" than a physics engine either exist in some form in Bullet and the rest, or aren't relevant for RL. The things you listed are engines that delegate to other projects for their physics simulation, and come with a ton of heavyweight baggage that you don't need to do RL.

This release includes four environments using the Fetch research platform and four environments using the ShadowHand robot. The manipulation tasks contained in these environments are significantly more difficult than the MuJoCo continuous control environments currently available in Gym, all of which are now easily solvable using recently released algorithms like PPO. Furthermore, our newly released environments use models of real robots and require the agent to solve realistic tasks. FetchReach-v0: Fetch has to move its end-effector to the desired goal position. FetchSlide-v0: Fetch has to hit a puck across a long table such that it slides and comes to rest on the desired goal.

By now, you have certainly taken part in the deep learning revolution, whether you're aware of it or not. If you use a voice-based personal assistant like Alexa, or any of the cloud-based web services, chances are advances in AI have already made it into their backend for companies to yield savings from processing efficiency. But sooner or later, deep learning is going to change your life. That, at least, is what Dr. Terry Sejnowski, the Francis Crick professor at the Salk Institute and a pioneer in deep learning, believes. "What we're creating is a whole new world," he argues. Like the impact of the internet going commercial in the 1990s, nobody can predict exactly what is going to change.

The goal of music highlight extraction is to get a short consecutive segment of a piece of music that provides an effective representation of the whole piece. In a previous work, we introduced an attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, for Pop songs. The rationale behind that approach is that the highlight of a song is usually the most emotional part. This paper extends our previous work in the following two aspects. First, methodology-wise we experiment with a new architecture that does not need any recurrent layers, making the training process faster. Moreover, we compare a late-fusion variant and an early-fusion variant to study which one better exploits the attention mechanism. Second, we conduct and report an extensive set of experiments comparing the proposed attention-based methods against a heuristic energy-based method, a structural repetition-based method, and a few other simple feature-based methods for this task. Due to the lack of public-domain labeled data for highlight extraction, following our previous work we use the RWC POP 100-song data set to evaluate how the detected highlights overlap with any chorus sections of the songs. The experiments demonstrate the effectiveness of our methods over competing methods. For reproducibility, we open source the code and pre-trained model at https://github.com/remyhuang/pop-music-highlighter/.

We propose a Multi-Instance-Learning (MIL) approach for weakly-supervised learning problems, where a training set is formed by bags (sets of feature vectors or instances) and only labels at bag-level are provided. Specifically, we consider the Multi-Instance Dynamic-Ordinal-Regression (MI-DOR) setting, where the instance labels are naturally represented as ordinal variables and bags are structured as temporal sequences. To this end, we propose Multi-Instance Dynamic Ordinal Random Fields (MI-DORF). In this framework, we treat instance-labels as temporally-dependent latent variables in an Undirected Graphical Model. Different MIL assumptions are modelled via newly introduced high-order potentials relating bag and instance-labels within the energy function of the model. We also extend our framework to address the Partially-Observed MI-DOR problems, where a subset of instance labels are available during training. We show on the tasks of weakly-supervised facial behavior analysis, Facial Action Unit (DISFA dataset) and Pain (UNBC dataset) Intensity estimation, that the proposed framework outperforms alternative learning approaches. Furthermore, we show that MIDORF can be employed to reduce the data annotation efforts in this context by large-scale.

Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge. Most approaches use backpropagation through time (BPTT), which is difficult to scale to very long sequences. This paper proposes a simple method that improves the ability to capture long term dependencies in RNNs by adding an unsupervised auxiliary loss to the original objective. This auxiliary loss forces RNNs to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel image classification with sequence lengths up to 16\,000, and a real document classification benchmark. Our results highlight good performance and resource efficiency of this approach over competitive baselines, including other recurrent models and a comparable sized Transformer. Further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization, as well as extreme cases where there is little to no backpropagation.

Phase retrieval (PR) algorithms have become an important component in many modern computational imaging systems. For instance, in the context of ptychography and speckle correlation imaging PR algorithms enable imaging past the diffraction limit and through scattering media, respectively. Unfortunately, traditional PR algorithms struggle in the presence of noise. Recently PR algorithms have been developed that use priors to make themselves more robust. However, these algorithms often require unrealistic (Gaussian or coded diffraction pattern) measurement models and offer slow computation times. These drawbacks have hindered widespread adoption. In this work we use convolutional neural networks, a powerful tool from machine learning, to regularize phase retrieval problems and improve recovery performance. We test our new algorithm, prDeep, in simulation and demonstrate that it is robust to noise, can handle a variety system models, and operates fast enough for high-resolution applications.