Search Results for author:
Found 17 papers, 1 papers with code
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models
Transformer-based Language Models have become ubiquitous in Natural Language Processing (NLP) due to their impressive performance on various tasks.
From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers
Building upon this insight, in this work, we propose a novel density loss that encourages higher activation sparsity (equivalently, lower activation density) in the pre-trained models.
LakeBench: Benchmarks for Data Discovery over Data Lakes
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery.
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Training deep networks and tuning hyperparameters on large datasets is computationally intensive.
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics
To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$.
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function.
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
We experiment on nonlinear synthetic functions and are able to accurately model as well as estimate feature attributions and even higher order terms in some cases, which is a testament to the representational power as well as interpretability of such architectures.
Multihop: Leveraging Complex Models to Learn Accurate Simple Models
In this paper, we propose a meta-approach where we transfer information from the complex model to the simple model by dynamically selecting and/or constructing a sequence of intermediate models of decreasing complexity that are less intricate than the original complex model.
Explainable artificial intelligence
Knowledge Distillation
+2
Learning to Rank Learning Curves
Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations.
Learning Global Transparent Models Consistent with Local Contrastive Explanations
Based on a key insight we propose a novel method where we create custom boolean features from sparse local contrastive explanations of the black-box model and then train a globally transparent model on just these, and showcase empirically that such models have higher local consistency compared with other known strategies, while still being close in performance to models that are trained with access to the original data.
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
Model Agnostic Contrastive Explanations for Structured Data
Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model.
A Survey on Neural Architecture Search
The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search.
Inductive Transfer for Neural Architecture Optimization
First, we propose a novel neural architecture selection method which employs this knowledge to identify strong and weak characteristics of neural architectures across datasets.
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
Understanding Unequal Gender Classification Accuracy from Face Images
Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender.
Neurology-as-a-Service for the Developing World
In this work, we present a cloud-based deep neural network approach to provide decision support for non-specialist physicians in EEG analysis and interpretation.
