An emerging alternative approach is replacing the manual design with automated Neural Architecture Search (NAS).

The diagram of our proposed Neural Lad framework is illustrated in Fig.1. The pseudo code of the proposed Neural Lad is described in Alg. 1. The training time of Neural Lad for the toy dataset is about 8s per epoch. It is worth noting that we use larger weight decay for PhysioNe sepsis dataset to avoid over-fitting. Visualization of memory network enhanced scores.

Neural networkmodels areknowntoreinforce hidden databiases, making them unreliable and difficult to interpret. We seek to build models that'know whatthey do not know' by introducing inductive biases in the function space.

In this paper, we propose HyperLogic: a flexible approach leveraging hypernetworks to generate weights of the main network. HyperLogic can be combined with existing differentiable rule learning methods to generate diverse rule sets, each capable ofcapturing heterogeneous patterns indata.

The form Equation (A.8) allows ustoapply chain rule tocalculate the gradient ofthe normalized Again, the chain rule is applied for the derivative of the weight matrix. Based on the gradient, one step of optimization under learning rateα could be expressed in a neat matrix multiplication format, decomposed by orthonormal basesU = {u1,u2,...}andV = {v1,v2,...}. The whole pruning framework is detailed in Algorithm 1. Grow fractionα is a function of training iterations that gradually decays forstability oftraining. ImageNet experiments are run on 8NVIDIATeslaV100s. Accordingly,thescheduleofAC/DCneed slight modifications based on the original setting.

In this work, we investigate the potential of weights to serve as effective representations, focusing on neural fields. Our key insight is that constraining the optimization space through a pre-trained base model and low-rank adaptation (LoRA) can induce structure in weight space. Across reconstruction, generation, and analysis tasks on 2D and 3D data, we find that multiplicative LoRA weights achieve high representation quality while exhibiting distinctiveness and semantic structure. When used with latent diffusion models, multiplicative LoRA weights enable higher-quality generation than existing weight-space methods.

We thank all the reviewers for the unanimous positive comments! Below we address questions raised by each reviewer. Q:"The operator on edges is never defined" " means stitching two adjacent paths (i.e., they share one endpoint) into a longer path. " Is this really an okay assump-6 Is anything given up by this assumption? Most parametric maps will not be injective." This is to define the cycle-consistency basis. Q:" It would be nice to show this form of cycle-consistency optimization enables novel capabilities, rather than Improved testing accuracy indicates better-learned representations.

Thank you for all the helpful comments. Several related works were raised by the reviewers which we discuss here. We note that the authors have marked their ArXiv submission as containing errors. Each of their inner loops uses SGD to solve the distance-regularized objectives. First, we use the EMA of slow weights to adjust the training parameters during optimization.