Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks. However, these results lack reliable uncertainty estimates. Conformal prediction methods provide a theoretical guarantee for node classification tasks, ensuring that the conformal prediction set contains the ground-truth label with a desired probability (e.g., 95\%). In this paper, we empirically show that for each node, aggregating the non-conformity scores of nodes with the same label can improve the efficiency of conformal prediction sets while maintaining valid marginal coverage. This observation motivates us to propose a novel algorithm named $\textit{Similarity-Navigated Adaptive Prediction Sets}$ (SNAPS), which aggregates the non-conformity scores based on feature similarity and structural neighborhood. The key idea behind SNAPS is that nodes with high feature similarity or direct connections tend to have the same label. By incorporating adaptive similar nodes information, SNAPS can generate compact prediction sets and increase the singleton hit ratio (correct prediction sets of size one). Moreover, we theoretically provide a finite-sample coverage guarantee of SNAPS. Extensive experiments demonstrate the superiority of SNAPS, improving the efficiency of prediction sets and singleton hit ratio while maintaining valid coverage.

Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates plasticity rules using a parameterized function, employing either truncated Taylor series for theoretical interpretability or multilayer perceptrons. These plasticity parameters are optimized via gradient descent over entire trajectories to align closely with observed neural activity or behavioral learning dynamics. This method can uncover complex rules that induce long nonlinear time dependencies, particularly involving factors like postsynaptic activity and current synaptic weights. We validate our approach through simulations, successfully recovering established rules such as Oja's, as well as more intricate plasticity rules with reward-modulated terms. We assess the robustness of our technique to noise and apply it to behavioral data from \textit{Drosophila} in a probabilistic reward-learning experiment. Notably, our findings reveal an active forgetting component in reward learning in flies, improving predictive accuracy over previous models. This modeling framework offers a promising new avenue for elucidating the computational principles of synaptic plasticity and learning in the brain.

Real-world decision-making tasks are usually partially observable Markov decision processes (POMDPs), where the state is not fully observable. Recent progress has demonstrated that recurrent reinforcement learning (RL), which consists of a context encoder based on recurrent neural networks (RNNs) for unobservable state prediction and a multilayer perceptron (MLP) policy for decision making, can mitigate partial observability and serve as a robust baseline for POMDP tasks. However, prior recurrent RL algorithms have faced issues with training instability. In this paper, we find that this instability stems from the autoregressive nature of RNNs, which causes even small changes in RNN parameters to produce large output variations over long trajectories.

In today's era, whatever we can measure at scale, we can optimize. So far, measuring the interpretability of units in deep neural networks (DNNs) for computer vision still requires direct human evaluation and is not scalable. As a result, the inner workings of DNNs remain a mystery despite the remarkable progress we have seen in their applications. In this work, we introduce the first scalable method to measure the per-unit interpretability in vision DNNs. This method does not require any human evaluations, yet its prediction correlates well with existing human interpretability measurements. We validate its predictive power through an interventional human psychophysics study. We demonstrate the usefulness of this measure by performing previously infeasible experiments: (1) A large-scale interpretability analysis across more than 70 million units from 835 computer vision models, and (2) an extensive analysis of how units transform during training. We find an anticorrelation between a model's downstream classification performance and per-unit interpretability, which is also observable during model training. Furthermore, we see that a layer's location and width influence its interpretability.

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity. To address this, one approach is to measure the similarity of activation responses to various inputs.Representational Similarity Matrices (RSMs) distill this similarity into scalar values for each input pair.These matrices encapsulate the entire similarity structure of a system, indicating which input lead to similar responses.While the similarity between images is ambiguous, we argue that the spatial location of semantic objects does neither influence human perception nor deep learning classifiers. Thus this should be reflected in the definition of similarity between image responses for computer vision systems. Revisiting the established similarity calculations for RSMs we expose their sensitivity to spatial alignment. In this paper we propose to solve this through, which are invariant to spatial permutation.

The best ways to maximize mobility and even prevent back pain. Walk, work and wake better with these hamstring stretches. Breakthroughs, discoveries, and DIY tips sent six days a week. We have some news you're gonna want to sit down for--but you probably shouldn't: Your hamstrings are, in all likelihood, an anatomical disaster for a number of possible reasons, not least of which being excessive time spent seated on them. "The hamstrings are three muscles located on the back of your thigh, and they're responsible for bending your knee and extending your hip," says Marissa Cummo, PT, DPT, assistant director of physical therapy at NYC Health + Hospitals Bellevue .

Hospital cyberattacks like the University of Mississippi Medical Center ransomware incident disrupt patient care. Ricardo Amper explains why healthcare systems are targets.

Smart glasses with AI photo editing capabilities raise questions about image authenticity as the technology can generate realistic backgrounds and alter photos instantly.

The new MAI-Image-2 model is rolling out on Copilot and Bing Image Creator, with standout photorealism and text-in-image capabilities.

A judge ordered Kalshi to immediately halt sports and election contracts in the state, intensifying a growing regulatory battle over prediction markets. Kalshi has been temporarily banned in Nevada, marking the latest escalation in the widening regulatory war over prediction markets. The First Judicial District Court of Nevada has issued a 14-day restraining order, effective immediately, barring the company from "offering a derivatives exchange and prediction market which offers event-based contracts relating to sports, election, and entertainment related events" without first obtaining gaming licenses. This is the first time a US state has forced the company to cease operations. This particular legal battle began just over a year ago, when Nevada regulators sent Kalshi a cease-and-desist letter demanding that it stop offering sports-related events contracts.