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Neural Template Regularization-Supplementary Material-Aditya V ora
Below are the details of each step. This allows us to input any number of images as input. This split is the same split that is used by [8]. This includes many scenes with complex architecture and backgrounds. We show additional results on the BlendedMVS dataset for 3 new objects.
DiViNeT: 3D Reconstruction from Disparate Views via Neural Template Regularization Aditya V ora
Several recent attempts have been made on neural reconstruction from sparse views.
ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP
In this work, we propose an innovative test-time poisoned sample detection framework that hinges on the in-terpretability of model predictions, grounded in the semantic meaning of inputs. We contend that triggers (e.g., infrequent words) are not supposed to fundamentally alter the underlying semantic meanings of poisoned samples as they want to
Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees Dohyeong Kim
However, the nonlinearity of risk measures makes it challenging to achieve convergence and optimality.
ConStat: Performance-Based Contamination Detection in Large Language Models Jasper Dekoninck 1, Mark Niklas Müller 1,2, Martin Vechev 1 Department of Computer Science 1
Public benchmarks play an essential role in the evaluation of large language models.
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.