So... when will our cars make a real-time calculation of our likelihood of a poor decision, leading to a collision, based on our current level of distractability or our agitation? Then they could communicate to all the neighboring cars something like "give this car a wider berth" and/or apply increasingly stringent restrictions on that driver,…
Learn how gradient descent optimizes neural networks — from the intuition of walking downhill to SGD, mini-batch, and learning rate selection
NNNLP 2026 : 2026 2nd International Conference on Neural Networks and Natural Language Processing
Neural Collaborative Filtering leverages deep neural networks to model user-item interactions, surpassing traditional methods in implicit feedback tasks
Zur erweiterten Suche English Zur erweiterten Suche Emotionserkennung bei Nachrichtenkommentaren mittels Convolutional Neural Networks und Label Propagationsverfahren Emotionserkennung bei Nachrichtenkommentaren mittels Convolutional Neural Networks und Label Propagationsverfahren Das Ziel dieser Arbeit ist es, anhand der textuellen Emotionserkennung einen Schulterschluss zwischen der Psychologie und der Computerlinguistik herzustellen. Gängige und in der Emotionserkennung verwendete Modelle werden
Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by consider
Analysis of neuromorphic computing architectures that mimic biological neural circuits, examining spiking neural networks, Intel Loihi, IBM TrueNorth, and implications for AI consciousness
This is the second post of a three-part series in which we derive the mathematics behind feedforward neural networks. We worked our way through forward and backward propagations in the first post, but if you remember, we only mentioned activation functions in passing. In particular, we did not derive an analytic expression for \(\pdv{a_{j, i}^{[l]}}{z_{j, i}^{[l]}}\) or, by extension, \(\pdv{J}{z_{j, i}^{[l]}}\). So let us pick up the derivations where we left off
# Linear Neural Networks ### Multiple regression Our car example showed how we could discover an optimal linear function for predicting one variable (fuel consumption) from one other (weight). Suppose now that we are also given one or more additional variables which could be useful as predictors. Our simple neureal network model can easily be extended to this case by adding more input units (Fig. 1). Similarly, we may want to predict more than one variable from the data that we're given. This can easily
Probabilistic Specifcation Verification of Neural Networks - sen-uni-kn/probspecs