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Self-Directed Task Identification

Timothy Gould, Sidike Paheding

Abstract

In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI is a minimal, interpretable framework demonstrating the feasibility of repurposing core machine learning concepts for a novel task structure. To our knowledge, no existing architectures have demonstrated this ability. Traditional approaches lack this capability, leaving data annotation as a time-consuming process that relies heavily on human effort. Using only standard neural network components, we show that SDTI can be achieved through appropriate problem formulation and architectural design. We evaluate the proposed framework on a range of benchmark tasks and demonstrate its effectiveness in reliably identifying the ground truth out of a set of potential target variables. SDTI outperformed baseline architectures by 14% in F1 score on synthetic task identification benchmarks. These proof-of-concept experiments highlight the future potential of SDTI to reduce dependence on manual annotation and to enhance the scalability of autonomous learning systems in real-world applications.

Self-Directed Task Identification

Abstract

In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI is a minimal, interpretable framework demonstrating the feasibility of repurposing core machine learning concepts for a novel task structure. To our knowledge, no existing architectures have demonstrated this ability. Traditional approaches lack this capability, leaving data annotation as a time-consuming process that relies heavily on human effort. Using only standard neural network components, we show that SDTI can be achieved through appropriate problem formulation and architectural design. We evaluate the proposed framework on a range of benchmark tasks and demonstrate its effectiveness in reliably identifying the ground truth out of a set of potential target variables. SDTI outperformed baseline architectures by 14% in F1 score on synthetic task identification benchmarks. These proof-of-concept experiments highlight the future potential of SDTI to reduce dependence on manual annotation and to enhance the scalability of autonomous learning systems in real-world applications.

Paper Structure

This paper contains 19 sections, 18 equations, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Experimental Design
  4. Datasets
  5. Training Process
  6. Testing Process
  7. Model Evaluation
  8. Limitations
  9. Model Structure
  10. Hyperparameter Generation
  11. SDTI layer
  12. Forward Propagation
  13. Updating Weights
  14. Predictions
  15. Algorithm Structures
  16. ...and 4 more sections

Figures (3)

  • Figure 1: Visual representation of Alg \ref{['alg:sdti_workflow']}. Within the outer loop the model randomly creates hyperparameters to be passed into the SDTI layer for the vecotrized ANN's running simultaneously. The SDTI layer will run for $S$ iterations and return the last cost value produced. These values are then reshaped and passed into an ArgMax function. This output is then saved in a specific data structure $T$ that is passed through another ArgMax function after $E$ iterations. The final output is a vector of integers indicating predicted target variable for a given dataset.
  • Figure 2: Stratified representation of mean values and variances for every SDTI epoch in all ablation study results that included 30 SDTI epochs.
  • Figure 3: Ablation study over SDTI hyperparameters. Each point represents one configuration in the (A,B,C) parameter space, where A is the number of training records, B is the number of epochs, and C is the number of SDTI iterations. Color intensity encodes the resulting F1 score. Regions with more than 500 records and 10+ epochs achieve near-perfect alignment (F1 $>$ 0.98), confirming the stability of SDTI under sufficient data and training time.