When you purchase through links in our articles, we may earn a small commission. Amazon's entry-level video doorbell delivers essential features at a bargain price. Limited local storage options (included Sync Module Core doesn't support USB storage) The Blink Video Doorbell (2nd Gen) delivers clear video, wide coverage, reliable alerts, and a long battery life at a remarkably low price. If you don't need advanced features like ultra-sharp resolution, or full-duplex audio, this doorbell is a true bargain. Blink is Amazon's budget line of smart home products.

We analyze neural scaling laws in a solvable model of last-layer fine-tuning where targets have intrinsic, instance-heterogeneous difficulty. In our Latent Instance Difficulty (LID) model, each input's target variance is governed by a latent ``precision'' drawn from a heavy-tailed distribution. While generalization loss recovers standard scaling laws, our main contribution connects this to inference. The pass@$k$ failure rate exhibits a power-law decay, $k^{-β_\text{eff}}$, but the observed exponent $β_\text{eff}$ is training-dependent. It grows with sample size $N$ before saturating at an intrinsic limit $β$ set by the difficulty distribution's tail. This coupling reveals that learning shrinks the ``hard tail'' of the error distribution: improvements in the model's generalization error steepen the pass@$k$ curve until irreducible target variance dominates. The LID model yields testable, closed-form predictions for this behavior, including a compute-allocation rule that favors training before saturation and inference attempts after. We validate these predictions in simulations and in two real-data proxies: CIFAR-10H (human-label variance) and a maths teacher-student distillation task.

Equivariant neural networks encode symmetry as an inductive bias and have achieved strong empirical performance in wide domains. However, their expressive power remains not well understood. Focusing on 2-layer ReLU networks, this paper investigates the impact of equiv-ariance constraints on the expressivity of equivariant and layer-wise equivariant networks. By examining the boundary hyperplanes and the channel vectors of ReLU networks, we construct an example showing that equivariance constraints could strictly limit expressive power. However, we demonstrate that this drawback can be compensated via enlarging the model size. Furthermore, we show that despite a larger model size, the resulting architecture could still correspond to a hypothesis space with lower complexity, implying superior generalizability for equivariant networks.

The latest Nest cams jump to 2K resolution, but what really elevates them is Gemini's pricey AI subscription smarts. All products featured on WIRED are independently selected by our editors. However, when you buy something through our retail links, we may earn an affiliate commission. Gemini can answer questions and offer descriptions. Overhauled Google Home app is much improved.

This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms.

The new Gen. 2 QuietComfort Ultra earbuds reinforce everything Bose active noise cancellation does right. We may earn revenue from the products available on this page and participate in affiliate programs. Washington-Dulles airport, red-eye to Berlin, time to kill and batteries to fill. Time was that would force a hard choice, because time was that the Bose QuietComfort Ultra Bluetooth earbuds didn't charge wirelessly. Drop the new QC Ultra Gen. 2 case on the Qi pad, however, and it blinks to life, no awkward adapters or extra plugs required.