AAbout Equation (1) As we discussed in Section 3, label smoothing and focal loss are equivalent to the standard CE loss with an additional maximum-entropy regularizer (see in Equation (1) and (2) in the main text). The proof of Equation (2) can be found in the corresponding paper [4]. SVHN is an image dataset which consists of 32 32 colored images of 0 9 digits. CIFAR-10 and CIFAR-100 consist of 32 32 colored natural images arranged in 10 and 100 classes, respectively. For 20Newsgroups, we use the GloVe word embedding [7] for text representation before the 1D-CNN model and set the embedding dimension as 100.

Capturing accurate uncertainty quantification of the predictions from deep neural networks is important in many real-world decision-making applications. A reliable predictor is expected to be accurate when it is confident about its predictions and indicate high uncertainty when it is likely to be inaccurate. However, modern neural networks have been found to be poorly calibrated, primarily in the direction of overconfidence. In recent years, there is a surge of research on model calibration by leveraging implicit or explicit regularization techniques during training, which achieve well calibration performance by avoiding overconfident outputs. In our study, we empirically found that despite the predictions obtained from these regularized models are better calibrated, they suffer from not being as calibratable, namely, it is harder to further calibrate these predictions with post-hoc calibration methods like temperature scaling and histogram binning. We conduct a series of empirical studies showing that overconfidence may not hurt final calibration performance if post-hoc calibration is allowed, rather, the penalty of confident outputs will compress the room of potential improvement in post-hoc calibration phase. Our experimental findings point out a new direction to improve calibration of DNNs by considering main training and post-hoc calibration as a unified framework.

The 31-year-old engineer and self-described indie game developer is suspected of firing shots at the annual event attended by President Donald Trump, high-profile media figures, and US government officials. US President Donald Trump listens as acting attorney general Todd Blanche speaks during a press briefing shortly after a shooting incident at the White House Correspondents' Dinner on April 25, 2026. A 31-year-old engineer and computer scientist was identified by media reports and President Donald Trump as the suspected shooter at the White House Correspondents Dinner on Saturday night. Cole Tomas Allen, of Torrance, California, was apprehended following the firing of shots at the Washington Hilton, where Trump was scheduled to deliver remarks to a ballroom full of journalists, cabinet officials, and Hilton staff. Allen's name surfaced in media reports shortly before Trump posted two photos of a suspect following his apprehension.

Artificial neural networks for motor control usually adopt generic architectures like fully connected MLPs. While general, these tabula rasa architectures rely on large amounts of experience to learn, are not easily transferable to new bodies, and have internal dynamics that are difficult to interpret. In nature, animals are born with highly structured connectivity in their nervous systems shaped by evolution; this innate circuitry acts synergistically with learning mechanisms to provide inductive biases that enable most animals to function well soon after birth and learn efficiently. Convolutional networks inspired by visual circuitry have encoded useful biases for vision. However, it is unknown the extent to which ANN architectures inspired by neural circuitry can yield useful biases for other AI domains. In this work, we ask what advantages biologically inspired ANN architecture can provide in the domain of motor control.

Linear sketches have been widely adopted to process fast data streams, and they can be used to accurately answer frequency estimation, approximate top K items, and summarize data distributions. When data are sensitive, it is desirable to provide privacy guarantees for linear sketches to preserve private information while delivering useful results with theoretical bounds. We show that linear sketches can ensure privacy and maintain their unique properties with a small amount of noise added at initialization. From the differentially private linear sketches, we showcase that the state-of-the-art quantile sketch in the turnstile model can also be private and maintain high performance. Experiments further demonstrate that our proposed differentially private sketches are quantitatively and qualitatively similar to noise-free sketches with high utilization on synthetic and real datasets.

Construct optimistic MDP fMk and compute optimistic policy πk (Algorithm 5). When the counter is 0 it gets (s,a), i.e., Ωi,e = (s,a,). When the counter is 1, we take (s,a) from ωn and map them to ωn/2 while eliminating half of the factors in consideration with the consistent scope Zi chosen by the policy (stored in factor 2d+ 1 + iof the state). It is handled similarly to the previous item, but considers the reward consistent scope zj chosen by the policy (stored in factor 3d+ 1 + j of the state). For i = 1,...,d, the i-th factor is taken from factor i of the previous state when the counter is not log n + 1, and otherwise performs the optimistic transition of factor i. Denote the value in the last factor of Ωi,e by ve, the policy's chosen scope by Zi (stored in factor 2d+ 1 + iof the state) and the policy's chosen next state direction by s0i (stored in factor d+ 1 + iof the state).

Monocular 3D detection of vehicle and infrastructure sides are two important topics in autonomous driving. Due to diverse sensor installations and focal lengths, researchers are faced with the challenge of constructing algorithms for the two topics based on different prior knowledge. In this paper, by taking into account the diversity of pitch angles and focal lengths, we propose a unified optimization target named normalized depth, which realizes the unification of 3D detection problems for the two sides. Furthermore, to enhance the accuracy of monocular 3D detection, 3D normalized cube depth of obstacle is developed to promote the learning of depth information. We posit that the richness of depth clues is a pivotal factor impacting the detection performance on both the vehicle and infrastructure sides. A richer set of depth clues facilitates the model to learn better spatial knowledge, and the 3D normalized cube depth offers sufficient depth clues. Extensive experiments demonstrate the effectiveness of our approach. Without introducing any extra information, our method, named MonoUNI, achieves state-of-the-art performance on five widely used monocular 3D detection benchmarks, including Rope3D and DAIR-V2X-I for the infrastructure side, KITTI and Waymo for the vehicle side, and nuScenes for the cross-dataset evaluation.