Search Results for author:
Found 24 papers, 17 papers with code
Scattered Forest Search: Smarter Code Space Exploration with LLMs
We propose a novel approach to scaling LLM inference for code generation.
Robust Semi-supervised Multimodal Medical Image Segmentation via Cross Modality Collaboration
With a channel-wise semantic consistency loss, our framework ensures alignment of modality-independent information from a feature-wise perspective across modalities, thereby fortifying it against misalignments in multimodal scenarios.
DALD: Improving Logits-based Detector without Logits from Black-box LLMs
However, these methods grapple with the misalignment between the distributions of the surrogate and the often undisclosed target models, leading to performance degradation, particularly with the introduction of new, closed-source models.
When and How Does In-Distribution Label Help Out-of-Distribution Detection?
We employ a graph-theoretic approach, rigorously analyzing the separability of ID data from OOD data in a closed-form manner.
Uncertainty Quantification for In-Context Learning of Large Language Models
Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning.
DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton
This paper introduces the retrieval-augmented large language model with Definite Finite Automaton (DFA-RAG), a novel framework designed to enhance the capabilities of conversational agents using large language models (LLMs).
How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection?
Subspace learning yields highly distinguishable distance measures between ID and OOD data.
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Open-world semi-supervised learning aims at inferring both known and novel classes in unlabeled data, by harnessing prior knowledge from a labeled set with known classes.
Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theory
Moving beyond OOD detection, ORL extends the capabilities of the model to not only detect unknown instances but also learn from and incorporate knowledge about these new classes.
Dream the Impossible: Outlier Imagination with Diffusion Models
Utilizing auxiliary outlier datasets to regularize the machine learning model has demonstrated promise for out-of-distribution (OOD) detection and safe prediction.
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis
This paper bridges the gap by providing an analytical framework to formalize and investigate when and how known classes can help discover novel classes.
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations.
Delving into Out-of-Distribution Detection with Vision-Language Representations
Recognizing out-of-distribution (OOD) samples is critical for machine learning systems deployed in the open world.
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature.
OpenCon: Open-world Contrastive Learning
Machine learning models deployed in the wild naturally encounter unlabeled samples from both known and novel classes.
Out-of-Distribution Detection with Deep Nearest Neighbors
In this paper, we explore the efficacy of non-parametric nearest-neighbor distance for OOD detection, which has been largely overlooked in the literature.
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?
Out-of-distribution (OOD) detection is a critical task for reliable machine learning.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
ReAct: Out-of-distribution Detection With Rectified Activations
Out-of-distribution (OOD) detection has received much attention lately due to its practical importance in enhancing the safe deployment of neural networks.
Ranked #13 on
Out-of-Distribution Detection
on ImageNet-1k vs SUN
DICE: Leveraging Sparsification for Out-of-Distribution Detection
Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world.
Ranked #4 on
Out-of-Distribution Detection
on ImageNet-1k vs SUN
DICE: A Simple Sparsification Method for Out-of-distribution Detection
Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world.
Adaptive Activation Thresholding: Dynamic Routing Type Behavior for Interpretability in Convolutional Neural Networks
In this paper, we show how a simple modification of the SGD scheme can help provide dynamic/EM routing type behavior in convolutional neural networks.
Interpretable Basis Decomposition for Visual Explanation
Explanations of the decisions made by a deep neural network are important for human end-users to be able to understand and diagnose the trustworthiness of the system.
Revisiting the Importance of Individual Units in CNNs via Ablation
We confirm that unit attributes such as class selectivity are a poor predictor for impact on overall accuracy as found previously in recent work \cite{morcos2018importance}.
