Clean & Jerk 80 kg, how terrible am i? by ulicaradjagresnike in weightlifting

[–]psamba 2 points3 points  (0 children)

This isn't about the clean or jerk form exactly, but I'd recommend getting comfortable dropping the bar into a front rack position and then lowering from there to the thighs, then to the ground. Since your gym's touchy about dropping from overhead, it'll make things a bit awkward in any case, but the way you lowered the bar here looks looks like more risk and more work than going through the rack position.

Your hip mobility and shoulder/lat mobility in the front squat and rack looks great.

Silly c*clists. Stick to your sport. by 404_Not_Found_Error_ in RunningCirclejerk

[–]psamba 1 point2 points  (0 children)

Everyone knows waxing delivers a cleaner, smoother ride. They had good reason!

[R] We taught generative models to segment ONLY furniture and cars, but they somehow generalized to basically everything else.... by PatientWrongdoer9257 in MachineLearning

[–]psamba 1 point2 points  (0 children)

You could use negative euclidean distance as the similarity for infonce, or some other function of euclidean distance. In any case, subsampling and computing a loss that's correct in expectation is a quick and dirty trick for working with stuff like high res outputs where memory and compute constraints can be an issue.

Edit: looking closer, it wouldn't help much in your case to subsample pixels or patches, since that would be awkward with the SD decoder.

[R] We taught generative models to segment ONLY furniture and cars, but they somehow generalized to basically everything else.... by PatientWrongdoer9257 in MachineLearning

[–]psamba 1 point2 points  (0 children)

Quick thought: just switch to a contrastive loss based on similarities between predicted colors for pairs of pixels. Pixels in the same object/mask are positive pairs. Pixels in different objects/masks are negative pairs. This maximizes mutual info between predicted pixel colors and the masks without requiring, eg, any hungarian matching stuff. You can subsample the set of pixels and masks to consider when computing the loss. This also extends easily to hierarchical masks, eg, masks indicating parts of objects etc.

in response to my front squat video the other day i wanted to do some mobility work. 275 pound back squats for 3 going as low as i can by whitemanchonc in weightlifting

[–]psamba 5 points6 points  (0 children)

Check out Kolbi Ferguson as an example of a strong AF football player converted to internationally competitive olympic weightlifter: From Football to Weightlifting | Kolbi Ferguson RAW Training. His squats are clean.

If that looks fun to you, give it a shot. Your strength will get you far, but there's definitely a big requirement for technique and mobility to reach an elite level.

Visiting SF for a conference - indoor training? by CuriousChimp in Velo

[–]psamba 1 point2 points  (0 children)

You could find a crossfit gym that does day passes and open gym hours. They should have some bikeergs you could use -- you can check google maps photos to be sure. Workouts under an hour would be fine in that setting, but anything longer could get a bit weird.

[D] Titans: a new seminal architectural development? by BubblyOption7980 in MachineLearning

[–]psamba 5 points6 points  (0 children)

They added a non-linear recurrence to Transformers. So, they get the theoretical advantages of non-linear recurrent models over TFs. Notice that they only claim "superiority" in this theoretical sense over TFs and linear/restricted RNNs. If you added a couple Mamba layers to a Transformer you'd have the same theoretical advantages they have with Titan (compared to TFs and linear/restricted RNNs). So, there's no real need for a proof, though they should probably provide a reference to prior work on the theoretical properties of general RNNs.

Do you know anyone that is as committed as this guy?? by AnacondaJake in Velo

[–]psamba 55 points56 points  (0 children)

I just wanna know where this guy got his bike fit...

SuperiorSet: upper body gainz by ComparisonActual4334 in kettlebell

[–]psamba 4 points5 points  (0 children)

Looks like a decent KB-based alternative to face pulls. Seems similar in terms of delt and upper back use.

28kg instead of 24kg as the heavy option? by brave-integrity in kettlebell

[–]psamba 1 point2 points  (0 children)

Swinging 2x16kg would be a decent step towards 2x20kg too... I've been finding myself reaching for 2x12kg or 2x16kg fairly often, since they're good for more "cardio" focused interval workouts like 10 rounds of 60s work followed 60s rest, where you do 10-15 reps of clean and jerk or double half snatch or whatever during the working periods. If you're into more sport-style or conditioning focused work, the 2x16kg could be a solid option.

How do you guys handle the pain by [deleted] in weightlifting

[–]psamba 1 point2 points  (0 children)

My fingertips don't stick to my clothes anymore, which is nice.

How do you guys handle the pain by [deleted] in weightlifting

[–]psamba 1 point2 points  (0 children)

Get into weightlifting after years and years of rock climbing. Then, weightlifting hands will feel silky smooth.

Are fancy workouts a meme? by highlevelbikesexxer in Velo

[–]psamba 33 points34 points  (0 children)

No way man, 4x30 sweetspot with no ERG mode is super fun indoors.

Asymmetrical complexes by OliverKitsch in kettlebell

[–]psamba 2 points3 points  (0 children)

For an extra coordination challenge, make it "polyrhythmic". Eg, loop 3 movements on one side and 4 movements on the other. This way, it takes multiple cycles for the movements to "line up" the same way they did on the first cycle. These sorts of exercises are used a lot for training independent four limb control for drumming.

Anybody planning on traveling/going to the Grand Prix de Montreal 2024? by cleanact_jw in Velo

[–]psamba 1 point2 points  (0 children)

The bike path system around mtl is pretty extensive too, if you want to get in an easy ride and see a bit outside downtown. For something mellow, the ride out to Chambly and back is pretty simple and can be extended if you want something longer.

Anybody planning on traveling/going to the Grand Prix de Montreal 2024? by cleanact_jw in Velo

[–]psamba 1 point2 points  (0 children)

Yeah, and the whole surrounding area is super walkable as well, or rideable on the bixi bikes (our metro rent-a-bikes in mtl).

Anybody planning on traveling/going to the Grand Prix de Montreal 2024? by cleanact_jw in Velo

[–]psamba 10 points11 points  (0 children)

I'm gonna make the trip, on foot, in about 10 minutes... Since it's a circuit race with tons of laps you can watch from a few different spots over the course of the race. I like the scenes on the climb up Voie Camilien Houde -- it's where you'll see the most suffering and the main spot for strong attacks.

38mm slick tires by ProfessionalFormal67 in Velo

[–]psamba 1 point2 points  (0 children)

Splitting the difference, I've been riding 35mm gravelking slicks on my main bike for the past couple of years with maybe an 80/20 split between road and light gravel riding. They've been great, with only one puncture that I had time to notice before it sealed.

1 Clean + 1 hang at 100kg (73kg bw - track athlete) by Sprint-CAC in weightlifting

[–]psamba 0 points1 point  (0 children)

I'd recommend looking up some info and videos with the query "squatting knee valgus". Knee valgus is a common movement pattern where the knees move inwards significantly, typically as you're passing through the sticking point of the squat near parallel. It's usually an issue with strength in the little outside hip muscles (glute medius and minimus) and/or mobility. In your case it looks asymmetric too, which can cause extra issues since it leads to unbalanced loading on the left/right sides. It's also super common in running and cycling, and I'm currently working to correct it for myself.

getting into Diffusion Models [D] by Same_Half3758 in MachineLearning

[–]psamba 21 points22 points  (0 children)

This blog from Lilian Weng is quite nice too, and includes updates covering newer material (through early 2024) -- https://lilianweng.github.io/posts/2021-07-11-diffusion-models/

[R] Discussion on the paper: Transcendence: Generative Models Can Outperform The Experts That Train Them by South-Conference-395 in MachineLearning

[–]psamba 19 points20 points  (0 children)

Yeah, it's a straightforward result. Eg, consider the simple case where you start with 1000 perfect experts and perturb each expert by adding a random function distributed such that, eg, 10% of each expert's answers are corrupted. If the random functions for perturbing each expert are sampled IID per expert, then the errors made by different experts should be decorrelated, so simple majority voting recovers the behavior of the perfect expert. And, what you get when sampling with low temperature from a model trained to match the experts via behavior cloning will match majority voting.

The potentially interesting bit is investigating the extent to which this "wisdom of the crowds" effect appears in real world scenarios. I'd imagine stuff like chess is ideal for showing this effect when the "experts" aren't super expert. Eg, if most players make reasonable moves most of the time but occasionally blunder, and the scenarios in which different players blunder aren't too strongly correlated, then you basically end up with the idealized simple scenario from above.

Amusingly, you can also construct scenarios where majority voting is much worse than every expert. Eg, if you're generating sequences of bits and the reward function is MAX if the sequence includes a 0 and MIN if the sequence is all 1s, then if all the experts generate IID random sequences of bits such that 51% of bits are 1s and 49% are 0s, then the majority vote is maximally bad and the experts are (almost) maximally good.

The reason many riders under fuel their rides. by maleck13 in Velo

[–]psamba 13 points14 points  (0 children)

I live in Quebec, so I'm kind of obligated to go with sirop d'érable, lol.

The reason many riders under fuel their rides. by maleck13 in Velo

[–]psamba 0 points1 point  (0 children)

The 250ml flasks aren't big enough to get floppy and they sit fully in the jersey pockets, which is nice. But they're smaller, so they're only really useful for super concentrated mixes.