When evaluating models, you should pick the smallest one that can deliver the accuracy you need. It will predict faster and require fewer hardware resources for training and inference. Frugality goes a long way.
Added some content to the paper
Meeting. Need to compare the stars from something like “%ethnic vegan%” that doesn’t appear much in the training set but shows up significantly in the later data and compare that to the gpt for the prompt “ethnic vegan”
Work with Aaron on document similarity
Add script section
Create initial maps
Plus one of heatmap
Fix bug that doesn’t save details
I have a much bigger application:
Got a primitive script generator working. Next will be to load it and navigate a map
Do another review, and do something in the introduction that works with the idea that we are barely individuals. How that changes from childhood through adulthood to old age, and how technology has had a huge impact
Run the new LIWC data and generate two spreadsheets. One with the word numbers and one with the default settings. Done
A central goal of artificial intelligence in high-stakes decision-making applications is to design a single algorithm that simultaneously expresses generalizability by learning coherent representations of their world and interpretable explanations of its dynamics. Here, we combine brain-inspired neural computation principles and scalable deep learning architectures to design compact neural controllers for task-specific compartments of a full-stack autonomous vehicle control system. We discover that a single algorithm with 19 control neurons, connecting 32 encapsulated input features to outputs by 253 synapses, learns to map high-dimensional inputs into steering commands. This system shows superior generalizability, interpretability and robust-ness compared with orders-of-magnitude larger black-box learning systems. The obtained neural agents enable high-fidelity autonomy for task-specific parts of a complex autonomous system.
In “Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning”, we introduce Uncertainty Baselines, a collection of high-quality implementations of standard and state-of-the-art deep learning methods for a variety of tasks, with the goal of making research on uncertainty and robustness more reproducible. The collection spans 19 methods across nine tasks, each with at least five metrics. Each baseline is a self-contained experiment pipeline with easily reusable and extendable components and with minimal dependencies outside of the framework in which it is written. The included pipelines are implemented in TensorFlow, PyTorch, and Jax. Additionally, the hyperparameters for each baseline have been extensively tuned over numerous iterations so as to provide even stronger results.
Twitter and Tear Gas
Adding optional buttons to TopicCombo so it’s possible to add a topic and not set a seed.
Need to check for the case where I am adding a topic to the group that provided the seed. No need to link to yourself
Create stories for 1) DB buildout 2) Model save/load 3) GML generation
9:30 sprint planning
Creating tables and getting Graph read/write to the DB
Rebuilt the code that takes into account the LIWC2015 components and how they relate/rollup. And I found one real difference between the ground truth and the gpt:
4:15 Meeting. Going to make a spreadsheet of the untrained GPT yelp, and be done with data. When that gets back, re-run the spreadsheets, and also add a version of the z-test code that produces rollups with the original LIWC data.
In this paper, we tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. By analyzing the literature, we found that current methods are either accurate or fast but not both which limits their usability in real world applications. To get the best of both aspects, we propose to mitigate the common shortcomings by following four design principles: decoupling the OOD detection from the segmentation task, observing the entire segmentation network instead of just its output, generating training data for the OOD detector by leveraging blind spots in the segmentation network and focusing the generated data on localized regions in the image to simulate OOD objects. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA). We validate the soundness of our approach across numerous ablation studies. We also show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
9:00 Sprint demos – done
More DB – working!
More GPT – got the seed response running. Now working on the topic response – done!
Added switching between raw content to support topics and details
Write 2 short reviews. Did some more topical overview and wrote a paragraph on Meltdown
Verify that subsequent parent-node linking works – done. That took a while. There was more stuff to fix
Make sure that we don’t make redundant links, just update weights – done. No weight updating for now. I think I’d rather calculate them on the fly for now
Make sure that details get stored with topics – done. Not sure that overwriting the response is a good idea. I think a better idea is to store the current response and replace it in the raw text once the details are set.
Get to_string() to show when node is selected – done
The size of scientific fields may impede the rise of new ideas. Examining 1.8 billion citations among 90 million papers across 241 subjects, we find a deluge of papers does not lead to turnover of central ideas in a field, but rather to ossification of canon. Scholars in fields where many papers are published annually face difficulty getting published, read, and cited unless their work references already widely cited articles. New papers containing potentially important contributions cannot garner field-wide attention through gradual processes of diffusion. These findings suggest fundamental progress may be stymied if quantitative growth of scientific endeavors—in number of scientists, institutes, and papers—is not balanced by structures fostering disruptive scholarship and focusing attention on novel ideas.
11:00 LAIC Tagup. Send Aaron a screenshot for the paper – done and integrated into report
Adding ForceNodes to MapGroups -done!
Adding connections. Done! That took a while. some of my states did not make sense, so the parent node would change. Need to test with successive iterations, but I’m done for the day