Explicit regularization and implicit bias in deep network classifiers trained with the square loss.Deep ReLU networks trained with the square loss have been observed to perform\nwell in classification tasks. We provide here a theoretical justification based\non analysis of the associated gradient flow....
Explicit on the other hand doesn’t have to converge each increment, but for the solution to be accurate time increments must be super small. Implicit models need a new measure. Difference Between Implicit vs Explicit Analysis The differences between the implicit and the explicit method. Learning...
Illustration of the ability of our new regularization to capture fine-scale details of shape while still stabilizing the optimization. Without a penalty on the mean curvature, our directional divergence term restores the shape more quickly and captures fine details (bottom). On the other hand, the...
Bridging the Gap Between Explicit and Implicit Representations: Cross-Data Association for VSLAM, TITS, 2024. [Paper] DNIV-SLAM: Neural Implicit Visual SLAM in Dynamic Environments, PRCV, 2024. [Paper] Improved End-to-End Multilevel NeRF-Based Dense RGB-D SLAM, PRCV, 2024. [Paper] MBA-SLA...
Spatially-Adaptive Pixelwise Networks for Fast Image Translation(Shaham et al. 2020) leverages a hybrid implicit-explicit representation for fast high-resolution image2image translation. Articulated representations NASA: Neural Articulated Shape Approximation(Deng et al. 2020) represents an articulated object...
Another advantage is that contrarily to the QεS space, the X -space is only formally defined and does not require further explicit integration rules. In this way, the γ5 will pertain to nS, and genuine dimensional identities cannot be applied, in general. This formal extension has however...
While knowing the ligand structure is not typically a limiting problem (at least for the case of two-dimensional representation), the reliance on traditional machine learning algorithms to map from these explicit features to a desired outcome requires many training examples. More reliable mapping comes...
explicit feedback, e.g. in terms of ratings. Never- theless, in real-world scenarios most feedback is not explicit but implicit. Implicit feedback is tracked au- tomatically, like monitoring clicks, view times, pur- chases, etc. Thus it is much easier to collect, because ...
of a clothed human based on local features FP: FP = [Fs(P), Fnb(P), Fnc(P)], (6) where Fs is the signed distance from a query point P to the closest body point Pb ∈ M, and Fnb is the barycentric surface normal of Pb; both provide strong regularization against self oc...
Similar to GAN, the new proposed model also has two parts, but are shown in two different ways: an energy function and a generator. We use only deep neural networks to represent both two models to avoid the need of explicit latent variables and inference over them as well as MCMC sampling...