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Possibly related to JuliaLang/julia#21370:
On 0.5.1:
```julia
julia> A = sprand(10,10,0.2);
julia> @benchmark 3.0*A
BenchmarkTools.Trial:
memory estimate: 672 bytes
allocs estimate: 4…
jebej updated
2 years ago
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**Describe the bug**
**Actual behaviour:** When I pass a `fun` lambda function to another function, the body of the lambda function is indented with 4 spaces when `max-indent` is explicitly set to …
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Very interesting work!
Is it possible to do for a multi-task learning problem? Say better than multi-task lasso in terms of speed?
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I am trying to adapt #154 to using a spike-slab prior `pi * delta + (1 - pi) * N(mu, sigma)` where pi, mu and sigma are given and a uniform prior will be used for the intercept. I came up with the co…
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An overview over all techniques/strategies mentioned:
1. Policy exploration/exploitation
- $\epsilon$-greedy
- Softmax
2. Update Q function
- SARSA
- (k-step) temporal differen…
luwo9 updated
2 months ago
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Most of methods in the list will be implemented in the order.
- inference for Sparse Gaussian process regression (based on JMLR 2005 "A unifying view of sparse approximate Gaussian process regression…
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I trained a yolo xs model and exported as onnx file.
I created the inference session by following the code below
```python
import onnxruntime as rt
sess = rt.InferenceSession(MODEL_PAT…
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I managed to get most algorithms running on the Sparse regression example, but not the DRLS algorithm. I guess it should be applicable to sparse regression, but what needs to be done with:
drls = Pro…
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e.g.
```
>>> python examples/sparse_regression.py --num-steps=2 --num-data=50 --num-dimensions 30
Traceback (most recent call last):
File "examples/sparse_regression.py", line 321, in
m…
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This is noticeable by comparing runtime on sparse vs dense synthetic regression datasets.
The sparse ones run much slower although intuitively they should run faster.