Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer
Authors: Seungwhan Moon, Jaime Carbonell
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of the proposed approaches via extensive simulations as well as a real-world application. and We show the efficacy of the proposed approaches on extensive simulation studies as well as a novel real-world transfer learning task. |
| Researcher Affiliation | Academia | Seungwhan Moon, Jaime Carbonell Language Technologies Institute School of Computer Science Carnegie Mellon University [seungwhm | jgc]@cs.cmu.edu |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. The paper describes methods using mathematical equations and textual explanations. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | Datasets: we use the RCV-1 dataset (English: 804,414 document; 116 classes) [Lewis et al., 2004], the 20 Newsgroups1 (English: 18,846 documents; 20 classes), the Reuters Multilingual [Amini et al., 2009] (French (FR): 26,648, Spanish (SP): 12,342, German (GR): 24,039, Italian (IT): 12,342 documents; 6 classes), and the R8 2 (English: 7,674 documents; 8 classes) datasets. |
| Dataset Splits | Yes | We obtain 5-fold results for each dataset generation, and report the overall average accuracy in Figure 4. and averaged over 10-fold runs. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, or memory specifications). |
| Software Dependencies | No | The paper mentions general techniques and models like word embeddings, knowledge graphs, and DNNs, but does not provide specific version numbers for any software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | For the following experiments, we set NS = NT = 4000 (number of samples), M = 4 (number of source and target dataset classes), MS = MT = 20 (original feature dimension), ME = 15 (embedded label space dimension), K = 12 (number of attention clusters), σdiff = 0.5, σlabel {0.05, 0.1, 0.2, 0.3}, and %LT {0.005, 0.01, 0.02, 0.05}. and ϵ is a fixed margin which we set as 0.1 and MC = 320, ME = 300, label: word embeddings and K = 40. |