Forecasting with multivariate time series, which aims to predict future values given previous and current several univariate time series data, has been studied for decades, with one example being ARIMA. Because it is difficult to measure the extent to which noise is mixed with informative signals within rapidly fluctuating financial time series data, designing a good predictive model is not a simple task. Recently, many researchers have become interested in recurrent neural networks and attention-based neur
Lab note Companion post to the Activation Functions carousel. Previously: Softmax: The Probability Engine. The previous Softmax post ended with a strange contrast. Softmax was beautifully smooth, but its Jacobian was dense, global, and a li...
Long short-term memory (LSTM) networks are a specialized type of recurrent neural network (RNN) designed to handle seque
This paper explores Generative Teaching Networks (GTN), which are similar to GANs but instead of compete, two networks cooperate on a task. With applications in multiple domains, GTNs can aid supervised learning training times, learn Reinforcement Learning tasks like cart-pole, and perform Neural Architecture Search (NAS
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AI技術の進化は目覚ましく、特に画像認識の分野ではConvolutional Neural Networks
NIPS 2017 Mon Dec 4th through Sat the 9th, 2017 at Long Beach Convention Center Paper ID: 3085 Title: Predictive State Recurrent Neural Networks ### Reviewer 1 This paper proposes a new model for dynamical systems (called PSRNN), which combines the frameworks of PSR and RNN non-trivially. The model is learned from data in two steps: The first step initialize the model parameters using two-stage regression (2SR), a method previously proposed by Hefny et al for learning PSRs. The second step use Back-pro
NIPS 2017 Mon Dec 4th through Sat the 9th, 2017 at Long Beach Convention Center Paper ID: 3085 Title: Predictive State Recurrent Neural Networks ### Reviewer 1 This paper proposes a new model for dynamical systems (called PSRNN), which combines the frameworks of PSR and RNN non-trivially. The model is learned from data in two steps: The first step initialize the model parameters using two-stage regression (2SR), a method previously proposed by Hefny et al for learning PSRs. The second step use Back-pro
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We
Do Graph Neural Network States Contain Graph Properties?Tom Pelletreau-Duris, Ruud van Bakel, Michael CochezDeep neural networks (DNNs) achieve sta