A classifier is a software component, often based upon deep learning (DL), that categorizes each input provided to it into one of a fixed set of classes. An IDK classifier may additionally output an 'I don't know' (IDK) on certain input. Given several different IDK classifiers for the same operation, the problem is considered of using them in concert in such a manner that the average duration to successfully classify any input is minimized. Optimal algorithms are proposed for solving this problem, both as is and under an additional constraint that the operation must be completed within a specified hard deadline
Machine learning algorithms have successfully entered industry through many real-world applications ...
Machine learning algorithms have successfully entered industry through many real-world applications ...
International audienceThis paper presents a novel framework for learning a soft-cascade detector wit...
A classifier is a software component, often based on Deep Learning, that categorizes each input prov...
An IDK classifier is a computing component that categorizes inputs into one of a number of classes, ...
Program synthesis is a term that describes a family of techniques that enables automatic generation ...
“Deep learning” uses Post-Selection—selection of a model after training multiple models using data. ...
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) a...
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) a...
In the recent decade, Intelligent Systems--advanced computer systems that can make useful prediction...
Classifier systems are massively parallel, message-passing, rule-based systems that learn through cr...
Recent studies have revealed that language model distillation can become less effective when there i...
We study the problem of multiclass classification with an extremely large number of classes, with th...
When a model makes a consequential decision, e.g., denying someone a loan, it needs to additionally ...
Predictive modeling using machine learning is an effective method for building compiler heuristics, ...
Machine learning algorithms have successfully entered industry through many real-world applications ...
Machine learning algorithms have successfully entered industry through many real-world applications ...
International audienceThis paper presents a novel framework for learning a soft-cascade detector wit...
A classifier is a software component, often based on Deep Learning, that categorizes each input prov...
An IDK classifier is a computing component that categorizes inputs into one of a number of classes, ...
Program synthesis is a term that describes a family of techniques that enables automatic generation ...
“Deep learning” uses Post-Selection—selection of a model after training multiple models using data. ...
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) a...
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) a...
In the recent decade, Intelligent Systems--advanced computer systems that can make useful prediction...
Classifier systems are massively parallel, message-passing, rule-based systems that learn through cr...
Recent studies have revealed that language model distillation can become less effective when there i...
We study the problem of multiclass classification with an extremely large number of classes, with th...
When a model makes a consequential decision, e.g., denying someone a loan, it needs to additionally ...
Predictive modeling using machine learning is an effective method for building compiler heuristics, ...
Machine learning algorithms have successfully entered industry through many real-world applications ...
Machine learning algorithms have successfully entered industry through many real-world applications ...
International audienceThis paper presents a novel framework for learning a soft-cascade detector wit...