Interpretable image recognition by constructing transparent embedding space
Humans usually explain their reasoning (eg classification) by dissecting the image and
pointing out the evidence from these parts to the concepts in their minds. Inspired by this …
pointing out the evidence from these parts to the concepts in their minds. Inspired by this …
Multinomial random forest
Despite the impressive performance of random forests (RF), its theoretical properties have
not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed …
not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed …
Mousika: Enable general in-network intelligence in programmable switches by knowledge distillation
Given the power efficiency and Tbps throughput of packet processing, several works are
proposed to offload the decision tree (DT) to programmable switches, ie, in-network …
proposed to offload the decision tree (DT) to programmable switches, ie, in-network …
Towards faithful xai evaluation via generalization-limited backdoor watermark
Saliency-based representation visualization (SRV)($ eg $, Grad-CAM) is one of the most
classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity …
classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity …
Born-again tree ensembles
T Vidal, M Schiffer - International conference on machine …, 2020 - proceedings.mlr.press
The use of machine learning algorithms in finance, medicine, and criminal justice can
deeply impact human lives. As a consequence, research into interpretable machine learning …
deeply impact human lives. As a consequence, research into interpretable machine learning …
Training interpretable convolutional neural networks by differentiating class-specific filters
Convolutional neural networks (CNNs) have been successfully used in a range of tasks.
However, CNNs are often viewed as “black-box” and lack of interpretability. One main …
However, CNNs are often viewed as “black-box” and lack of interpretability. One main …
Fast sparse decision tree optimization via reference ensembles
Sparse decision tree optimization has been one of the most fundamental problems in AI
since its inception and is a challenge at the core of interpretable machine learning. Sparse …
since its inception and is a challenge at the core of interpretable machine learning. Sparse …
Optimizing deep learning inference on embedded systems through adaptive model selection
Deep neural networks (DNNs) are becoming a key enabling technique for many application
domains. However, on-device inference on battery-powered, resource-constrained …
domains. However, on-device inference on battery-powered, resource-constrained …
Tnt: An interpretable tree-network-tree learning framework using knowledge distillation
Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the
trained DNNs easy to use, but they remain an ambiguous decision process for every test …
trained DNNs easy to use, but they remain an ambiguous decision process for every test …
Empowering in-network classification in programmable switches by binary decision tree and knowledge distillation
Given the high packet processing efficiency of programmable switches (eg, P4 switches of
Tbps), several works are proposed to offload the decision tree (DT) to P4 switches for in …
Tbps), several works are proposed to offload the decision tree (DT) to P4 switches for in …