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YellowFin is a learning rate and momentum tuner motivated by robustness properties and analysis of quadratic objectives. It stems from a known but obscure fact: the momentum operator's spectral radius is constant in a large subset of the hyperparameter space. For quadratic objectives, the optimizer tunes both the learning rate and the momentum to keep the hyperparameters within a region in which the convergence rate is a constant rate equal to the root momentum. This notion is extended empirically to non-convex objectives. On every iteration, YellowFin optimizes the hyperparameters to minimize a local quadratic optimization.

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using transfer learning to transfer knowledge from one building to predict the energy consumption of another building with scarce data

The discrete analogue of the Gabor transform is obtained by choosing the temporal window function in a time-frequency analysis as the discrete analogue of the Gaussian kernel.

Generalized State-Dependent Exploration, or gSDE, is an exploration method for reinforcement learning that uses more general features and re-sampling the noise periodically. State-Dependent Exploration (SDE) is an intermediate solution for exploration that consists in adding noise as a function of the state $s\_{t}$, to the deterministic action $\mu\left(\mathbf{s}\_{t}\right)$. At the beginning of an episode, the parameters $\theta\_{\epsilon}$ of that exploration function are drawn from a Gaussian distribution. The resulting action $\mathbf{a}\_{t}$ is as follows: $$ \mathbf{a}\_{t}=\mu\left(\mathbf{s}\_{t} ; \theta\_{\mu}\right)+\epsilon\left(\mathbf{s}\_{t} ; \theta\_{\epsilon}\right), \quad \theta\_{\epsilon} \sim \mathcal{N}\left(0, \sigma^{2}\right) $$ This episode-based exploration is smoother and more consistent than the unstructured step-based exploration. Thus, during one episode, instead of oscillating around a mean value, the action a for a given state $s$ will be the same. In the case of a linear exploration function $\epsilon\left(\mathbf{s} ; \theta\_{\epsilon}\right)=\theta\_{\epsilon} \mathbf{s}$, by operation on Gaussian distributions, Rückstieß et al. show that the action element $\mathbf{a}\_{j}$ is normally distributed: $$ \pi]_{j}\left(\mathbf{a}\_{j} \mid \mathbf{s}\right) \sim \mathcal{N}\left(\mu\_{j}(\mathbf{s}), \hat{\sigma\_{j}}^{2}\right) $$ where $\hat{\sigma}$ is a diagonal matrix with elements $\hat{\sigma}\_{j}=\sqrt{\sum\_{i}\left(\sigma\_{i j} \mathbf{s}\_{i}\right)^{2}}$. Because we know the policy distribution, we can obtain the derivative of the log-likelihood $\log \pi(\mathbf{a} \mid \mathbf{s})$ with respect to the variance $\sigma$ : $$ \frac{\partial \log \pi(\mathbf{a} \mid \mathbf{s})}{\partial \sigma_{i j}}=\frac{\left(\mathbf{a}\_{j}-\mu\_{j}\right)^{2}-\hat{\sigma\_{j}}^{2}}{\hat{\sigma}\_{j}^{3}} \frac{\mathbf{s}\_{i}^{2} \sigma\_{i j}}{\hat{\sigma_{j}}} $$ This can be easily plugged into the likelihood ratio gradient estimator, which allows to adapt $\sigma$ during training. SDE is therefore compatible with standard policy gradient methods, while addressing most shortcomings of the unstructured exploration. For gSDE, two improvements are suggested: 1. We sample the parameters $\theta\_{\epsilon}$ of the exploration function every $n$ steps instead of every episode. 2. Instead of the state s, we can in fact use any features. We chose policy features $\mathbf{z}\_{\mu}\left(\mathbf{s} ; \theta\_{\mathbf{z}\_{\mu}}\right)$ (last layer before the deterministic output $\left.\mu(\mathbf{s})=\theta\_{\mu} \mathbf{z}\_{\mu}\left(\mathbf{s} ; \theta_{\mathbf{z}\_{\mu}}\right)\right)$ as input to the noise function $\epsilon\left(\mathbf{s} ; \theta\_{\epsilon}\right)=\theta\_{\epsilon} \mathbf{z}\_{\mu}(\mathbf{s})$

A resnet-like architecture with deconvolution feature normalization (Ye et al. 2020, ICLR) layers in the first few layers for sparse low-level feature identification, and batch normalization layers in the later layers.

Integrated Gaussian derivative kernels are obtained by integrating the continuous Gaussian derivative kernels over each pixel support region. In this way, some of the severe artefacts of sampling the Gaussian derivative kernels at too fine scales can be reduced.

lda2vec builds representations over both words and documents by mixing word2vec’s skipgram architecture with Dirichlet-optimized sparse topic mixtures. The Skipgram Negative-Sampling (SGNS) objective of word2vec is modified to utilize document-wide feature vectors while simultaneously learning continuous document weights loading onto topic vectors. The total loss term $L$ is the sum of the Skipgram Negative Sampling Loss (SGNS) $L^{neg}\_{ij}$ with the addition of a Dirichlet-likelihood term over document weights, $L\_{d}$. The loss is conducted using a context vector, $\overrightarrow{c\_{j}}$ , pivot word vector $\overrightarrow{w\_{j}}$, target word vector $\overrightarrow{w\_{i}}$, and negatively-sampled word vector $\overrightarrow{w\_{l}}$: $$ L = L^{d} + \Sigma\_{ij}L^{neg}\_{ij} $$ $$L^{neg}\_{ij} = \log\sigma\left(c\_{j}\cdot\overrightarrow{w\_{i}}\right) + \sum^{n}\_{l=0}\sigma\left(-\overrightarrow{c\_{j}}\cdot\overrightarrow{w\_{l}}\right)$$

BARThez is a self-supervised transfer learning model for the French language based on BART. Compared to existing BERT-based French language models such as CamemBERT and FlauBERT, BARThez is well-suited for generative tasks, since not only its encoder but also its decoder is pretrained.

A Multiplicative RNN (mRNN) is a type of recurrent neural network with multiplicative connections. In a standard RNN, the current input $x\_{t}$ is first transformed via the visible-to-hidden weight matrix $W\_{hx}$ and then contributes additively to the input for the current hidden state. An mRNN allows the current input (a character in the original example) to affect the hidden state dynamics by determining the entire hidden-to-hidden matrix (which defines the non-linear dynamics) in addition to providing an additive bias. To achieve this goal, the authors modify the RNN so that its hidden-to-hidden weight matrix is a (learned) function of the current input $x\_{t}$: $$ h\_{t} = \tanh\left(W\_{hx}x\_{t} + W\_{hh}^{\left(x\_{y}\right)}h\_{t-1} + b\_{h}\right)$$ $$ o\_{t} = W\_{oh}h\_{t} + b\_{o} $$ This is the same as the equations for a standard RNN, except that $W\_{hh}$ is replaced with $W^{(xt)}\_{hh}$. allowing each input (character) to specify a different hidden-to-hidden weight matrix.

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A neural network model to automatically capture trends in time-series data for improved prediction/forecasting performance

The training contains two steps. The first step is to train data for creating the T2D and D2T models. Then, in the second step, use these models to infer self-memory for self-training the D2T model. Then, the training takes self-memory and new data for self-training the D2T model but not the T2D model.

The time-causal and time-recursive analogue of the Gabor transform provides a way to define a Gabor-like time-frequency analysis for real-time signals, for which the future cannot be accessed. This is achieved by choosing the temporal window function in a windowed Fourier transform as the time-causal limit kernel, which is a temporal kernel that is (i) time-causal, (ii) time-recursive and (iii) obeys temporal scale covariance.

uNet neural network architecture which takes multiple (X) tensors as input and contains Spatial Transformer units (ST)

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