Release brulee 1.0.0 · tidymodels/brulee

New models for tabular data:

Regularization Learning Networks (brulee_rln()) use a conventional MLP architecture but each weight learns its own adaptive regularization coefficient.
ResNet (brulee_resnet()) can fit a multilayer neural network with skip (i.e. residual) connections and batch normalization.
AutoInt (brulee_auto_int()) uses residual connections and columnwise attention mechanisms to create embeddings that encourage in-context learning of features.
Saint (brulee_saint()) uses column and/or row attention mechanisms.
Chronos2 (brulee_chronos()) is a foundational model for forecasting.
All modeling functions now support GPU acceleration via the device parameter. Users can specify device = "cpu", device = "cuda", or device = "mps" (Apple Silicon). When device = NULL (default), the package automatically selects CUDA if available, otherwise defaults to CPU. Note: MPS is not auto-selected because it doesn't support float64 dtype required by brulee. See?training_efficiency for some related notes.

Breaking Changes

Float tensors were changed from 64-bit floats to 32-bit. This is to enable GPU usage on MPS devices.
Parameters are initialized on CPU devices and then converted to the chosen device. In some cases, the RNG initialization code is independent of the seed.
For classification, the softmax was moved out of every model's forward pass so the loss can use torch::nnf_cross_entropy() (which applies the log-sum-exp trick internally) instead of nll_loss(log(softmax(x))). This avoids log(0) underflow that produced NaN losses and "numerical overflow" early stopping on overspecified brulee_saint() / brulee_auto_int() fits. Affects brulee_mlp(), brulee_logistic_reg(), brulee_multinomial_reg(), brulee_resnet(), brulee_auto_int(), and brulee_saint(). New fits carry output_type = "logits" so the predict path applies softmax; serialized fits from earlier versions of brulee continue to predict correctly.