Your monitor needs a monitor arm, and I've been testing every single one I can get my hands on to see which is best. A monitor arm should be one of those simple products you buy once and never think about again. But I've seen horror stories of cheap, knock-off models that collapse, damaging both the desk and the monitor. Anything that mounts a very heavy piece of expensive tech like a high-end monitor should be high-quality, which is true of all the options below. Each of the monitor arms on our list have been hand-tested by us. Most are currently clamped down to a desk of one of our product reviewers.

Optimal transport (OT) defines a powerful framework to compare probability distributions in a geometrically faithful way. However, the practical impact of OT is still limited because of its computational burden. We propose a new class of stochastic optimization algorithms to cope with large-scale problems routinely encountered in machine learning applications. These methods are able to manipulate arbitrary distributions (either discrete or continuous) by simply requiring to be able to draw samples from them, which is the typical setup in high-dimensional learning problems.

Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical distributed approaches, synchronous or asynchronous, are based on the parameter server architecture, i.e., worker nodes compute gradients which are communicated to the parameter server while updated parameters are returned. Recently, distributed training with AllReduce operations gained popularity as well. While many of those operations seem appealing, little is reported about wall-clock training time improvements. In this paper, we carefully analyze the AllReduce based setup, propose timing models which include network latency, bandwidth, cluster size and compute time, and demonstrate that a pipelined training with a width of two combines the best of both synchronous and asynchronous training. Specifically, for a setup consisting of a four-node GPU cluster we show wall-clock time training improvements of up to 5.4x compared to conventional approaches.

Our approach is purely based on 2D convolutions. Nevertheless, it3 outperforms or performs comparably to many more costly 3D models. We thank the reviewers for pointing out some related (or missing) references. The12 Timeception layers involve group convolutions at different time scales while our TAM layers only use depthwise13 convolution. As a result, the Timeception has significantly more parameters than the TAM (10% vs. 0.1% of the14 totalmodelparameters).

Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and If you ran experiments... (a) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? Please refer to both main text and appendix for experiment details. Did you report error bars (e.g., with respect to the random seed after running experiments multiple All adaptation experiments in Procgen and RLBench are run for 3 seeds. Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal As stated in section 2, we use RTX A5000 GPUs each with 24GB memory. C2F-ARM algorithm and training framework are built based on the original author's implementation Did you mention the license of the assets?

Previous work has achieved encouraging performance.