Search Results for author:
Found 16 papers, 7 papers with code
LLM meets Vision-Language Models for Zero-Shot One-Class Classification
In this setting, only the label of the target class is available, and the goal is to discriminate between positive and negative query samples without requiring any validation example from the target task.
A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models
In recent years, the rapid evolution of computer vision has seen the emergence of various foundation models, each tailored to specific data types and tasks.
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning
In this paper, we present a novel approach that leverages text-derived statistics to predict the mean and covariance of the visual feature distribution for each class.
Order-Preserving GFlowNets
To address these issues, we propose Order-Preserving GFlowNets (OP-GFNs), which sample with probabilities in proportion to a learned reward function that is consistent with a provided (partial) order on the candidates, thus eliminating the need for an explicit formulation of the reward function.
DBsurf: A Discrepancy Based Method for Discrete Stochastic Gradient Estimation
Computing gradients of an expectation with respect to the distributional parameters of a discrete distribution is a problem arising in many fields of science and engineering.
Efficient Training of Deep Equilibrium Models
Deep equilibrium models (DEQs) have proven to be very powerful for learning data representations.
A Statistical Model for Predicting Generalization in Few-Shot Classification
Such a set is hardly available in few-shot learning scenarios, a highly disregarded shortcoming in the field.
DNN Quantization with Attention
We call this procedure \textit{DNN Quantization with Attention} (DQA).
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools.
Efficient Sampling for Predictor-Based Neural Architecture Search
Usually, we compute the proxy for all DNNs in the network search space and pick those that maximize the proxy as candidates for optimization.
Iteratively Training Look-Up Tables for Network Quantization
For each layer, we learn a value dictionary and an assignment matrix to represent the network weights.
Mixed Precision DNNs: All you need is a good parametrization
Since choosing the optimal bitwidths is not straight forward, training methods, which can learn them, are desirable.
Training Variational Auto Encoders with Discrete Latent Representations using Importance Sampling
In this paper, we propose an easy method to train VAEs with binary or categorically valued latent representations.
Deep Neural Network inference with reduced word length
Deep neural networks (DNN) are powerful models for many pattern recognition tasks, yet their high computational complexity and memory requirement limit them to applications on high-performance computing platforms.
A Machine-learning framework for automatic reference-free quality assessment in MRI
Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest.
Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements
The problem of identifying end-use electrical appliances from their individual consumption profiles, known as the appliance identification problem, is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated plug-wise metering.
