Adaptive Neural Networks for Efficient Inference
Authors: Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, these approaches yield dramatic reductions in computational cost, with up to a 2.8x speedup on state-of-the-art networks from the Image Net image recognition challenge with minimal (< 1%) loss of top5 accuracy. |
| Researcher Affiliation | Collaboration | 1Boston University, Boston, MA, USA 2Amazon, Cambridge, MA, USA 3Microsoft Research, Redmond, WA, USA. |
| Pseudocode | Yes | Algorithm 1 Adaptive Network Learning Pseudocode |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We evaluate our method on the Imagenet 2012 classification dataset (Russakovsky et al., 2015) which has 1000 object classes. We train using the 1.28 million training images and evaluate the system using 50k validation images. |
| Dataset Splits | Yes | We train using the 1.28 million training images and evaluate the system using 50k validation images. |
| Hardware Specification | Yes | We measure network times using the built-in tool in the Caffe library on a server that utilizes a Nvidia Titan X Pascal with Cu DNN 5. |
| Software Dependencies | Yes | We measure network times using the built-in tool in the Caffe library on a server that utilizes a Nvidia Titan X Pascal with Cu DNN 5. |
| Experiment Setup | Yes | To output a prediction following each convolutional layer, we train a single layer linear classifier after a global average pooling for each layer... We sweep the cost trade-off parameter in the range 0.0 to 0.1 to achieve different budget points. |