Learning to accurately represent environmental uncertainty is crucial for adaptive and optimal behaviors in various cognitive tasks. However, it remains unclear how the human brain, constrained by finite cognitive resources, internalise the highly structured environmental uncertainty. In this study, we explore how these learned distributions deviate from the ground truth, resulting in observable inconsistency in a novel structured density estimation task. During each trial, human participants were asked to learn and report the latent probability distribution functions underlying sequentially presented independent observations. As the number of observations increased, the reported predictive density became closer to the ground truth. Nevertheless, we observed an intriguing inconsistency in human structure estimation, specifically a large error in the number of reported clusters.

Efficient coding theory hasbeen used topredict howthe brain should allocate its limited resources in these scenarios by removing redundant information [1-4].

Data from naturalistic systems are often modeled asgenerated from an underlying low dimensional manifold embedded inahighdimensional measurement space.

Privacy issue is a main concern in developing face recognition techniques.

Technology Engineering Thomas Edison's failed rechargeable battery may get a second life The famed inventor's nickel-iron idea isn't suited for EVs, but it could help solar farms and data centers. Breakthroughs, discoveries, and DIY tips sent six days a week. A rechargeable battery based on technology pioneered by Thomas Edison may finally get its due. But while the famous inventor envisioned nickel-iron batteries powering the automobile industry over a century ago, researchers now believe the underlying concepts are more suited for renewable energy centers. According to a study published in the journal, a team including engineers from the University of California, Los Angeles have developed a prototype battery that recharges in seconds and withstands over 12,000 cycles of use--an equivalent to over 30 years of daily activity.

Federated Dropout) and reduces the gap between model-heterogeneous and model-homogeneous FL, especially under the large-model large-dataset regime.

Traditional robust reinforcement learning often depends on rectangularity assumptions, where adverse probability measures of outcome states are assumed to be independent across different states and actions.

The proposed approach may be unsuitable for datasets with very less number of classes. When number of classes are low,our Insight 3(main paper) may not hold, making the pretexttask very difficult tolearn. This might make technology more accessible to organizations and individuals with limited resources. It can also aid applications where data is protected by privacy regulations and hence difficult to collect. The negative consequences might include making these systems more availabletoorganizations orindividuals whotrytoutilize them forillegalpurposes.