We investigate a sequential optimization procedure to minimize the empiricalrisk functional fbθ(x) = 12kGbθ(x) yk2 for certain families of deep networks Gθ(x).

We investigate a sequential optimization procedure to minimize the empirical risk functional $f_{\hat\theta}(x) = \frac{1}{2}\|G_{\hat\theta}(x) - y\|^2$ for certain families of deep networks $G_{\theta}(x)$. The approach is to optimize a sequence of objective functions that use network parameters obtained during different stages of the training process. When initialized with random parameters $\theta_0$, we show that the objective $f_{\theta_0}(x)$ is ``nice'' and easy to optimize with gradient descent. As learning is carried out, we obtain a sequence of generative networks $x \mapsto G_{\theta_t}(x)$ and associated risk functions $f_{\theta_t}(x)$, where $t$ indicates a stage of stochastic gradient descent during training. Since the parameters of the network do not change by very much in each step, the surface evolves slowly and can be incrementally optimized. The algorithm is formalized and analyzed for a family of expansive networks. We call the procedure {\it surfing} since it rides along the peak of the evolving (negative) empirical risk function, starting from a smooth surface at the beginning of learning and ending with a wavy nonconvex surface after learning is complete. Experiments show how surfing can be used to find the global optimum and for compressed sensing even when direct gradient descent on the final learned network fails.

The paper proposes a new method for provably fitting deep generative models to observations, a highly non-convex optimization problem. Instead of trying to find the latent code that explains the measurements directly, as proposed by Bora et al. this paper starts with a different deep generative model that has random weights, for which Hand et al. showed that gradient descent provably works. Then they incrementally modify the weights of the generator to approach the true generator while using the previous optimum as a starting point. This sequence of models can be snapshots of the model during the training process. The main result is a theory that shows that a warm-started non convex optimization in expansive Gaussian networks yields successful recovery.

We investigate a sequential optimization procedure to minimize the empirical risk functional f_{\hat\theta}(x) \frac{1}{2}\ G_{\hat\theta}(x) - y\ 2 for certain families of deep networks G_{\theta}(x) . The approach is to optimize a sequence of objective functions that use network parameters obtained during different stages of the training process. When initialized with random parameters \theta_0, we show that the objective f_{\theta_0}(x) is nice'' and easy to optimize with gradient descent. As learning is carried out, we obtain a sequence of generative networks x \mapsto G_{\theta_t}(x) and associated risk functions f_{\theta_t}(x), where t indicates a stage of stochastic gradient descent during training. Since the parameters of the network do not change by very much in each step, the surface evolves slowly and can be incrementally optimized.

We investigate a sequential optimization procedure to minimize the empirical risk functional $f_{\hat\theta}(x) \frac{1}{2}\ G_{\hat\theta}(x) - y\ 2$ for certain families of deep networks $G_{\theta}(x)$. The approach is to optimize a sequence of objective functions that use network parameters obtained during different stages of the training process. When initialized with random parameters $\theta_0$, we show that the objective $f_{\theta_0}(x)$ is nice'' and easy to optimize with gradient descent. As learning is carried out, we obtain a sequence of generative networks $x \mapsto G_{\theta_t}(x)$ and associated risk functions $f_{\theta_t}(x)$, where $t$ indicates a stage of stochastic gradient descent during training. Since the parameters of the network do not change by very much in each step, the surface evolves slowly and can be incrementally optimized.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Accelerating trends in artificial intelligence (AI) and robotics point to significant economic disruption in the years ahead. Together, machine learning, natural-language recognition, biometrics, and decision management are converging toward what the World Economic Forum has described as the Fourth Industrial Revolution. To this point, technology has consistently generated more jobs than it destroys--but many now wonder if "this time is different." According to McKinsey & Company, half of all existing work activities could be automated by currently existing technologies, saving some $16 trillion in wages. Forecasts indicate that revenues from AI will expand from the current $8 billion to more than $47 billion by 2020.

Peck and his team asked study participants to wear headbands fitted with two functional near-infrared spectroscopy (fNIRS) probes that measured activity in the prefrontal cortex, a region of the brain that plays a critical role in the emotion and reasoning behind decision-making. Each person was given a list of films culled from IMDB's lineup of the 250 best movies and the 100 worst movies and asked to pick the top and bottom three movies. The participants were then shown slides of each selection, while the fNIRS probes measured the person's neural patterns that correlated with preference and opposition. "We try to get an idea of what the patterns in the brain look like for things they like or don't like," said Peck. Preference patterns were then fed into a brain-computer recommendation system -- a series of filters and machine-learning algorithms -- that interpreted those patterns to make recommendations as subjects watched a fresh series of movie slides.

Researchers from the Massachusetts Institute of Technology recently presented their new artificially intelligent system that can fill the information gap itself by surfing the Internet. During the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, MIT researchers said the AI system has the ability to gather structured information from unstructured machine readable documents automatically. Karthik Nasarimhan, one of the co-authors of the study, said that in order for them to do this, they employed a technique called reinforcement learning where the system learns through the notion of cumulative reward. This technique was based on behavioral psychology and is also used in swarm intelligence, game theory, and genetic algorithms among others. According to Nasarimhan, the technique is necessary because there is a lot of contrasting information out which can cause uncertainty when the data is merged.

We live in an age where there is a vast, over-abundance of data available on the web. The problem is that sifting through all of it to find and make sense of whatever is deemed relevant is an incredibly time-consuming task. But it may soon become easier, as Massachusetts Institute of Technology researchers recently revealed in a paper that introduces a new artificial intelligence system that would be capable of learning, on its own, in extracting useful information from online sources. Recently presented at the conference of the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing in Austin, the researchers' paper describes a new information extraction system that's able to automatically extract structured information from unstructured machine-readable documents. Put simply, the program can do what humans are good at: When faced with a gap in information or something we don't understand, we go and search for another document to digest that will add to our understanding or further our knowledge.